Overview

Brought to you by YData

Dataset statistics

Number of variables57
Number of observations1168
Missing cells207
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory529.2 KiB
Average record size in memory464.0 B

Variable types

Numeric14
Categorical42
Boolean1

Alerts

BldgType is highly overall correlated with KitchenAbvGr and 1 other fieldsHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtFullBath and 1 other fieldsHigh correlation
BsmtFullBath is highly overall correlated with BsmtFinSF1High correlation
BsmtQual is highly overall correlated with Neighborhood and 1 other fieldsHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1High correlation
ExterQual is highly overall correlated with KitchenQual and 1 other fieldsHigh correlation
Exterior1st is highly overall correlated with Exterior2ndHigh correlation
Exterior2nd is highly overall correlated with Exterior1stHigh correlation
GarageCond is highly overall correlated with GarageQualHigh correlation
GarageQual is highly overall correlated with GarageCondHigh correlation
HalfBath is highly overall correlated with MSSubClassHigh correlation
HouseStyle is highly overall correlated with MSSubClassHigh correlation
KitchenAbvGr is highly overall correlated with BldgType and 1 other fieldsHigh correlation
KitchenQual is highly overall correlated with ExterQual and 1 other fieldsHigh correlation
LotArea is highly overall correlated with LotFrontageHigh correlation
LotFrontage is highly overall correlated with LotArea and 1 other fieldsHigh correlation
MSSubClass is highly overall correlated with BldgType and 3 other fieldsHigh correlation
MSZoning is highly overall correlated with NeighborhoodHigh correlation
Neighborhood is highly overall correlated with BsmtQual and 1 other fieldsHigh correlation
OverallQual is highly overall correlated with BsmtQual and 3 other fieldsHigh correlation
Utilities is highly overall correlated with LotFrontageHigh correlation
YearRemodAdd is highly overall correlated with OverallQualHigh correlation
MSZoning is highly imbalanced (58.2%) Imbalance
Street is highly imbalanced (96.0%) Imbalance
LandContour is highly imbalanced (68.5%) Imbalance
Utilities is highly imbalanced (99.0%) Imbalance
LandSlope is highly imbalanced (78.5%) Imbalance
Condition1 is highly imbalanced (71.5%) Imbalance
Condition2 is highly imbalanced (95.9%) Imbalance
BldgType is highly imbalanced (59.0%) Imbalance
RoofStyle is highly imbalanced (65.6%) Imbalance
RoofMatl is highly imbalanced (94.7%) Imbalance
ExterCond is highly imbalanced (72.6%) Imbalance
BsmtCond is highly imbalanced (77.1%) Imbalance
BsmtFinType2 is highly imbalanced (69.6%) Imbalance
Heating is highly imbalanced (93.4%) Imbalance
CentralAir is highly imbalanced (66.6%) Imbalance
Electrical is highly imbalanced (78.8%) Imbalance
KitchenAbvGr is highly imbalanced (81.6%) Imbalance
Functional is highly imbalanced (80.6%) Imbalance
GarageQual is highly imbalanced (85.8%) Imbalance
GarageCond is highly imbalanced (88.0%) Imbalance
PavedDrive is highly imbalanced (72.1%) Imbalance
SaleType is highly imbalanced (75.5%) Imbalance
SaleCondition is highly imbalanced (62.1%) Imbalance
LotFrontage has 202 (17.3%) missing values Missing
MasVnrArea has 678 (58.0%) zeros Zeros
BsmtFinSF1 has 384 (32.9%) zeros Zeros
BsmtUnfSF has 95 (8.1%) zeros Zeros
2ndFlrSF has 671 (57.4%) zeros Zeros
WoodDeckSF has 601 (51.5%) zeros Zeros
OpenPorchSF has 502 (43.0%) zeros Zeros

Reproduction

Analysis started2025-03-13 22:21:56.708713
Analysis finished2025-03-13 22:22:22.770631
Duration26.06 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

MSSubClass
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.686644
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:22.860468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.469076
Coefficient of variation (CV)0.74919016
Kurtosis1.577851
Mean56.686644
Median Absolute Deviation (MAD)30
Skewness1.4128849
Sum66210
Variance1803.6224
MonotonicityNot monotonic
2025-03-13T17:22:22.958469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 438
37.5%
60 243
20.8%
50 110
 
9.4%
120 66
 
5.7%
30 53
 
4.5%
160 52
 
4.5%
90 45
 
3.9%
80 45
 
3.9%
70 43
 
3.7%
190 24
 
2.1%
Other values (5) 49
 
4.2%
ValueCountFrequency (%)
20 438
37.5%
30 53
 
4.5%
40 3
 
0.3%
45 9
 
0.8%
50 110
 
9.4%
60 243
20.8%
70 43
 
3.7%
75 11
 
0.9%
80 45
 
3.9%
85 18
 
1.5%
ValueCountFrequency (%)
190 24
 
2.1%
180 8
 
0.7%
160 52
 
4.5%
120 66
 
5.7%
90 45
 
3.9%
85 18
 
1.5%
80 45
 
3.9%
75 11
 
0.9%
70 43
 
3.7%
60 243
20.8%

MSZoning
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
RL
930 
RM
169 
FV
 
51
RH
 
11
C (all)
 
7

Length

Max length7
Median length2
Mean length2.0299658
Min length2

Characters and Unicode

Total characters2371
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL 930
79.6%
RM 169
 
14.5%
FV 51
 
4.4%
RH 11
 
0.9%
C (all) 7
 
0.6%

Length

2025-03-13T17:22:23.071553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:23.175678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
rl 930
79.1%
rm 169
 
14.4%
fv 51
 
4.3%
rh 11
 
0.9%
c 7
 
0.6%
all 7
 
0.6%

Most occurring characters

ValueCountFrequency (%)
R 1110
46.8%
L 930
39.2%
M 169
 
7.1%
F 51
 
2.2%
V 51
 
2.2%
l 14
 
0.6%
H 11
 
0.5%
C 7
 
0.3%
7
 
0.3%
( 7
 
0.3%
Other values (2) 14
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1110
46.8%
L 930
39.2%
M 169
 
7.1%
F 51
 
2.2%
V 51
 
2.2%
l 14
 
0.6%
H 11
 
0.5%
C 7
 
0.3%
7
 
0.3%
( 7
 
0.3%
Other values (2) 14
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1110
46.8%
L 930
39.2%
M 169
 
7.1%
F 51
 
2.2%
V 51
 
2.2%
l 14
 
0.6%
H 11
 
0.5%
C 7
 
0.3%
7
 
0.3%
( 7
 
0.3%
Other values (2) 14
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1110
46.8%
L 930
39.2%
M 169
 
7.1%
F 51
 
2.2%
V 51
 
2.2%
l 14
 
0.6%
H 11
 
0.5%
C 7
 
0.3%
7
 
0.3%
( 7
 
0.3%
Other values (2) 14
 
0.6%

LotFrontage
Real number (ℝ)

High correlation  Missing 

Distinct107
Distinct (%)11.1%
Missing202
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean70.783644
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:23.286423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q160
median70
Q380.75
95-th percentile108.75
Maximum313
Range292
Interquartile range (IQR)20.75

Descriptive statistics

Standard deviation23.99585
Coefficient of variation (CV)0.33900275
Kurtosis12.1034
Mean70.783644
Median Absolute Deviation (MAD)10
Skewness1.6747915
Sum68377
Variance575.80081
MonotonicityNot monotonic
2025-03-13T17:22:23.413431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 108
 
9.2%
80 60
 
5.1%
70 54
 
4.6%
75 42
 
3.6%
50 41
 
3.5%
65 39
 
3.3%
85 32
 
2.7%
78 23
 
2.0%
90 18
 
1.5%
55 16
 
1.4%
Other values (97) 533
45.6%
(Missing) 202
 
17.3%
ValueCountFrequency (%)
21 16
1.4%
24 16
1.4%
30 4
 
0.3%
32 5
 
0.4%
34 9
0.8%
35 8
0.7%
36 5
 
0.4%
37 3
 
0.3%
38 1
 
0.1%
40 11
0.9%
ValueCountFrequency (%)
313 1
0.1%
182 1
0.1%
174 2
0.2%
168 1
0.1%
160 1
0.1%
153 1
0.1%
152 1
0.1%
150 1
0.1%
149 1
0.1%
144 1
0.1%

LotArea
Real number (ℝ)

High correlation 

Distinct897
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10597.211
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:23.549529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3420.7
Q17598.25
median9512.5
Q311649.75
95-th percentile17335.95
Maximum215245
Range213945
Interquartile range (IQR)4051.5

Descriptive statistics

Standard deviation10615.651
Coefficient of variation (CV)1.0017401
Kurtosis197.02639
Mean10597.211
Median Absolute Deviation (MAD)2012.5
Skewness12.369074
Sum12377542
Variance1.1269204 × 108
MonotonicityNot monotonic
2025-03-13T17:22:23.675982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9600 19
 
1.6%
7200 18
 
1.5%
8400 14
 
1.2%
6000 10
 
0.9%
9000 10
 
0.9%
10800 9
 
0.8%
6120 8
 
0.7%
7500 7
 
0.6%
9100 7
 
0.6%
8125 6
 
0.5%
Other values (887) 1060
90.8%
ValueCountFrequency (%)
1300 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.2%
1596 1
 
0.1%
1680 6
0.5%
1890 1
 
0.1%
1920 1
 
0.1%
1936 1
 
0.1%
1950 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
53504 1
0.1%
53107 1
0.1%
46589 1
0.1%
45600 1
0.1%
40094 1
0.1%

Street
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Pave
1163 
Grvl
 
5

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4672
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave

Common Values

ValueCountFrequency (%)
Pave 1163
99.6%
Grvl 5
 
0.4%

Length

2025-03-13T17:22:23.791635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:23.890564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pave 1163
99.6%
grvl 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
v 1168
25.0%
P 1163
24.9%
a 1163
24.9%
e 1163
24.9%
G 5
 
0.1%
r 5
 
0.1%
l 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 1168
25.0%
P 1163
24.9%
a 1163
24.9%
e 1163
24.9%
G 5
 
0.1%
r 5
 
0.1%
l 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 1168
25.0%
P 1163
24.9%
a 1163
24.9%
e 1163
24.9%
G 5
 
0.1%
r 5
 
0.1%
l 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 1168
25.0%
P 1163
24.9%
a 1163
24.9%
e 1163
24.9%
G 5
 
0.1%
r 5
 
0.1%
l 5
 
0.1%

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Reg
738 
IR1
387 
IR2
 
36
IR3
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3504
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIR2
2nd rowIR1
3rd rowIR1
4th rowReg
5th rowReg

Common Values

ValueCountFrequency (%)
Reg 738
63.2%
IR1 387
33.1%
IR2 36
 
3.1%
IR3 7
 
0.6%

Length

2025-03-13T17:22:23.984503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:24.077509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
reg 738
63.2%
ir1 387
33.1%
ir2 36
 
3.1%
ir3 7
 
0.6%

Most occurring characters

ValueCountFrequency (%)
R 1168
33.3%
e 738
21.1%
g 738
21.1%
I 430
 
12.3%
1 387
 
11.0%
2 36
 
1.0%
3 7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1168
33.3%
e 738
21.1%
g 738
21.1%
I 430
 
12.3%
1 387
 
11.0%
2 36
 
1.0%
3 7
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1168
33.3%
e 738
21.1%
g 738
21.1%
I 430
 
12.3%
1 387
 
11.0%
2 36
 
1.0%
3 7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1168
33.3%
e 738
21.1%
g 738
21.1%
I 430
 
12.3%
1 387
 
11.0%
2 36
 
1.0%
3 7
 
0.2%

LandContour
Categorical

Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Lvl
1050 
Bnk
 
49
HLS
 
39
Low
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3504
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl 1050
89.9%
Bnk 49
 
4.2%
HLS 39
 
3.3%
Low 30
 
2.6%

Length

2025-03-13T17:22:24.180097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:24.274628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
lvl 1050
89.9%
bnk 49
 
4.2%
hls 39
 
3.3%
low 30
 
2.6%

Most occurring characters

ValueCountFrequency (%)
L 1119
31.9%
v 1050
30.0%
l 1050
30.0%
B 49
 
1.4%
n 49
 
1.4%
k 49
 
1.4%
H 39
 
1.1%
S 39
 
1.1%
o 30
 
0.9%
w 30
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 1119
31.9%
v 1050
30.0%
l 1050
30.0%
B 49
 
1.4%
n 49
 
1.4%
k 49
 
1.4%
H 39
 
1.1%
S 39
 
1.1%
o 30
 
0.9%
w 30
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 1119
31.9%
v 1050
30.0%
l 1050
30.0%
B 49
 
1.4%
n 49
 
1.4%
k 49
 
1.4%
H 39
 
1.1%
S 39
 
1.1%
o 30
 
0.9%
w 30
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 1119
31.9%
v 1050
30.0%
l 1050
30.0%
B 49
 
1.4%
n 49
 
1.4%
k 49
 
1.4%
H 39
 
1.1%
S 39
 
1.1%
o 30
 
0.9%
w 30
 
0.9%

Utilities
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
AllPub
1167 
NoSeWa
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters7008
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub 1167
99.9%
NoSeWa 1
 
0.1%

Length

2025-03-13T17:22:24.375552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:24.462132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
allpub 1167
99.9%
nosewa 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 2334
33.3%
A 1167
16.7%
P 1167
16.7%
u 1167
16.7%
b 1167
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2334
33.3%
A 1167
16.7%
P 1167
16.7%
u 1167
16.7%
b 1167
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2334
33.3%
A 1167
16.7%
P 1167
16.7%
u 1167
16.7%
b 1167
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2334
33.3%
A 1167
16.7%
P 1167
16.7%
u 1167
16.7%
b 1167
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

LotConfig
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Inside
842 
Corner
210 
CulDSac
 
77
FR2
 
35
FR3
 
4

Length

Max length7
Median length6
Mean length5.9657534
Min length3

Characters and Unicode

Total characters6968
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCulDSac
2nd rowCorner
3rd rowCorner
4th rowInside
5th rowInside

Common Values

ValueCountFrequency (%)
Inside 842
72.1%
Corner 210
 
18.0%
CulDSac 77
 
6.6%
FR2 35
 
3.0%
FR3 4
 
0.3%

Length

2025-03-13T17:22:24.568563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:24.673573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
inside 842
72.1%
corner 210
 
18.0%
culdsac 77
 
6.6%
fr2 35
 
3.0%
fr3 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 1052
15.1%
n 1052
15.1%
I 842
12.1%
s 842
12.1%
i 842
12.1%
d 842
12.1%
r 420
 
6.0%
C 287
 
4.1%
o 210
 
3.0%
S 77
 
1.1%
Other values (9) 502
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1052
15.1%
n 1052
15.1%
I 842
12.1%
s 842
12.1%
i 842
12.1%
d 842
12.1%
r 420
 
6.0%
C 287
 
4.1%
o 210
 
3.0%
S 77
 
1.1%
Other values (9) 502
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1052
15.1%
n 1052
15.1%
I 842
12.1%
s 842
12.1%
i 842
12.1%
d 842
12.1%
r 420
 
6.0%
C 287
 
4.1%
o 210
 
3.0%
S 77
 
1.1%
Other values (9) 502
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1052
15.1%
n 1052
15.1%
I 842
12.1%
s 842
12.1%
i 842
12.1%
d 842
12.1%
r 420
 
6.0%
C 287
 
4.1%
o 210
 
3.0%
S 77
 
1.1%
Other values (9) 502
7.2%

LandSlope
Categorical

Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Gtl
1105 
Mod
 
51
Sev
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3504
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl

Common Values

ValueCountFrequency (%)
Gtl 1105
94.6%
Mod 51
 
4.4%
Sev 12
 
1.0%

Length

2025-03-13T17:22:24.781165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:24.869592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gtl 1105
94.6%
mod 51
 
4.4%
sev 12
 
1.0%

Most occurring characters

ValueCountFrequency (%)
G 1105
31.5%
t 1105
31.5%
l 1105
31.5%
M 51
 
1.5%
o 51
 
1.5%
d 51
 
1.5%
S 12
 
0.3%
e 12
 
0.3%
v 12
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1105
31.5%
t 1105
31.5%
l 1105
31.5%
M 51
 
1.5%
o 51
 
1.5%
d 51
 
1.5%
S 12
 
0.3%
e 12
 
0.3%
v 12
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1105
31.5%
t 1105
31.5%
l 1105
31.5%
M 51
 
1.5%
o 51
 
1.5%
d 51
 
1.5%
S 12
 
0.3%
e 12
 
0.3%
v 12
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1105
31.5%
t 1105
31.5%
l 1105
31.5%
M 51
 
1.5%
o 51
 
1.5%
d 51
 
1.5%
S 12
 
0.3%
e 12
 
0.3%
v 12
 
0.3%

Neighborhood
Categorical

High correlation 

Distinct25
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
NAmes
172 
CollgCr
132 
OldTown
86 
Edwards
73 
Somerst
71 
Other values (20)
634 

Length

Max length7
Median length7
Mean length6.4982877
Min length5

Characters and Unicode

Total characters7590
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSomerst
2nd rowNAmes
3rd rowEdwards
4th rowSawyer
5th rowCollgCr

Common Values

ValueCountFrequency (%)
NAmes 172
14.7%
CollgCr 132
 
11.3%
OldTown 86
 
7.4%
Edwards 73
 
6.2%
Somerst 71
 
6.1%
Sawyer 64
 
5.5%
Gilbert 63
 
5.4%
NWAmes 63
 
5.4%
NridgHt 62
 
5.3%
SawyerW 48
 
4.1%
Other values (15) 334
28.6%

Length

2025-03-13T17:22:24.978192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names 172
14.7%
collgcr 132
 
11.3%
oldtown 86
 
7.4%
edwards 73
 
6.2%
somerst 71
 
6.1%
sawyer 64
 
5.5%
gilbert 63
 
5.4%
nwames 63
 
5.4%
nridght 62
 
5.3%
sawyerw 48
 
4.1%
Other values (15) 334
28.6%

Most occurring characters

ValueCountFrequency (%)
r 751
 
9.9%
e 724
 
9.5%
l 516
 
6.8%
o 393
 
5.2%
d 383
 
5.0%
s 381
 
5.0%
m 349
 
4.6%
C 349
 
4.6%
N 335
 
4.4%
w 324
 
4.3%
Other values (28) 3085
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 751
 
9.9%
e 724
 
9.5%
l 516
 
6.8%
o 393
 
5.2%
d 383
 
5.0%
s 381
 
5.0%
m 349
 
4.6%
C 349
 
4.6%
N 335
 
4.4%
w 324
 
4.3%
Other values (28) 3085
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 751
 
9.9%
e 724
 
9.5%
l 516
 
6.8%
o 393
 
5.2%
d 383
 
5.0%
s 381
 
5.0%
m 349
 
4.6%
C 349
 
4.6%
N 335
 
4.4%
w 324
 
4.3%
Other values (28) 3085
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 751
 
9.9%
e 724
 
9.5%
l 516
 
6.8%
o 393
 
5.2%
d 383
 
5.0%
s 381
 
5.0%
m 349
 
4.6%
C 349
 
4.6%
N 335
 
4.4%
w 324
 
4.3%
Other values (28) 3085
40.6%

Condition1
Categorical

Imbalance 

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Norm
1006 
Feedr
 
66
Artery
 
39
RRAn
 
22
PosN
 
16
Other values (4)
 
19

Length

Max length6
Median length4
Mean length4.1232877
Min length4

Characters and Unicode

Total characters4816
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowRRAn
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1006
86.1%
Feedr 66
 
5.7%
Artery 39
 
3.3%
RRAn 22
 
1.9%
PosN 16
 
1.4%
RRAe 7
 
0.6%
PosA 6
 
0.5%
RRNn 5
 
0.4%
RRNe 1
 
0.1%

Length

2025-03-13T17:22:25.103881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:25.219623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
norm 1006
86.1%
feedr 66
 
5.7%
artery 39
 
3.3%
rran 22
 
1.9%
posn 16
 
1.4%
rrae 7
 
0.6%
posa 6
 
0.5%
rrnn 5
 
0.4%
rrne 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1150
23.9%
N 1028
21.3%
o 1028
21.3%
m 1006
20.9%
e 179
 
3.7%
A 74
 
1.5%
R 70
 
1.5%
F 66
 
1.4%
d 66
 
1.4%
t 39
 
0.8%
Other values (4) 110
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1150
23.9%
N 1028
21.3%
o 1028
21.3%
m 1006
20.9%
e 179
 
3.7%
A 74
 
1.5%
R 70
 
1.5%
F 66
 
1.4%
d 66
 
1.4%
t 39
 
0.8%
Other values (4) 110
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1150
23.9%
N 1028
21.3%
o 1028
21.3%
m 1006
20.9%
e 179
 
3.7%
A 74
 
1.5%
R 70
 
1.5%
F 66
 
1.4%
d 66
 
1.4%
t 39
 
0.8%
Other values (4) 110
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1150
23.9%
N 1028
21.3%
o 1028
21.3%
m 1006
20.9%
e 179
 
3.7%
A 74
 
1.5%
R 70
 
1.5%
F 66
 
1.4%
d 66
 
1.4%
t 39
 
0.8%
Other values (4) 110
 
2.3%

Condition2
Categorical

Imbalance 

Distinct8
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Norm
1154 
Feedr
 
5
RRNn
 
2
PosN
 
2
Artery
 
2
Other values (3)
 
3

Length

Max length6
Median length4
Mean length4.0077055
Min length4

Characters and Unicode

Total characters4681
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1154
98.8%
Feedr 5
 
0.4%
RRNn 2
 
0.2%
PosN 2
 
0.2%
Artery 2
 
0.2%
PosA 1
 
0.1%
RRAe 1
 
0.1%
RRAn 1
 
0.1%

Length

2025-03-13T17:22:25.354718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:25.472350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
norm 1154
98.8%
feedr 5
 
0.4%
rrnn 2
 
0.2%
posn 2
 
0.2%
artery 2
 
0.2%
posa 1
 
0.1%
rrae 1
 
0.1%
rran 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1163
24.8%
N 1158
24.7%
o 1157
24.7%
m 1154
24.7%
e 13
 
0.3%
R 8
 
0.2%
F 5
 
0.1%
d 5
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1163
24.8%
N 1158
24.7%
o 1157
24.7%
m 1154
24.7%
e 13
 
0.3%
R 8
 
0.2%
F 5
 
0.1%
d 5
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1163
24.8%
N 1158
24.7%
o 1157
24.7%
m 1154
24.7%
e 13
 
0.3%
R 8
 
0.2%
F 5
 
0.1%
d 5
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1163
24.8%
N 1158
24.7%
o 1157
24.7%
m 1154
24.7%
e 13
 
0.3%
R 8
 
0.2%
F 5
 
0.1%
d 5
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

BldgType
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
1Fam
974 
TwnhsE
 
90
Duplex
 
45
Twnhs
 
34
2fmCon
 
25

Length

Max length6
Median length4
Mean length4.3030822
Min length4

Characters and Unicode

Total characters5026
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row2fmCon
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam 974
83.4%
TwnhsE 90
 
7.7%
Duplex 45
 
3.9%
Twnhs 34
 
2.9%
2fmCon 25
 
2.1%

Length

2025-03-13T17:22:25.601676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:25.710760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1fam 974
83.4%
twnhse 90
 
7.7%
duplex 45
 
3.9%
twnhs 34
 
2.9%
2fmcon 25
 
2.1%

Most occurring characters

ValueCountFrequency (%)
m 999
19.9%
1 974
19.4%
a 974
19.4%
F 974
19.4%
n 149
 
3.0%
T 124
 
2.5%
w 124
 
2.5%
h 124
 
2.5%
s 124
 
2.5%
E 90
 
1.8%
Other values (10) 370
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 999
19.9%
1 974
19.4%
a 974
19.4%
F 974
19.4%
n 149
 
3.0%
T 124
 
2.5%
w 124
 
2.5%
h 124
 
2.5%
s 124
 
2.5%
E 90
 
1.8%
Other values (10) 370
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 999
19.9%
1 974
19.4%
a 974
19.4%
F 974
19.4%
n 149
 
3.0%
T 124
 
2.5%
w 124
 
2.5%
h 124
 
2.5%
s 124
 
2.5%
E 90
 
1.8%
Other values (10) 370
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 999
19.9%
1 974
19.4%
a 974
19.4%
F 974
19.4%
n 149
 
3.0%
T 124
 
2.5%
w 124
 
2.5%
h 124
 
2.5%
s 124
 
2.5%
E 90
 
1.8%
Other values (10) 370
 
7.4%

HouseStyle
Categorical

High correlation 

Distinct8
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
1Story
583 
2Story
358 
1.5Fin
121 
SLvl
 
50
SFoyer
 
32
Other values (3)
 
24

Length

Max length6
Median length6
Mean length5.9143836
Min length4

Characters and Unicode

Total characters6908
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Story
2nd row1Story
3rd row1Story
4th row1Story
5th row1Story

Common Values

ValueCountFrequency (%)
1Story 583
49.9%
2Story 358
30.7%
1.5Fin 121
 
10.4%
SLvl 50
 
4.3%
SFoyer 32
 
2.7%
1.5Unf 11
 
0.9%
2.5Unf 7
 
0.6%
2.5Fin 6
 
0.5%

Length

2025-03-13T17:22:25.837070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:25.957844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1story 583
49.9%
2story 358
30.7%
1.5fin 121
 
10.4%
slvl 50
 
4.3%
sfoyer 32
 
2.7%
1.5unf 11
 
0.9%
2.5unf 7
 
0.6%
2.5fin 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
S 1023
14.8%
o 973
14.1%
r 973
14.1%
y 973
14.1%
t 941
13.6%
1 715
10.4%
2 371
 
5.4%
F 159
 
2.3%
5 145
 
2.1%
. 145
 
2.1%
Other values (8) 490
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6908
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1023
14.8%
o 973
14.1%
r 973
14.1%
y 973
14.1%
t 941
13.6%
1 715
10.4%
2 371
 
5.4%
F 159
 
2.3%
5 145
 
2.1%
. 145
 
2.1%
Other values (8) 490
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6908
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1023
14.8%
o 973
14.1%
r 973
14.1%
y 973
14.1%
t 941
13.6%
1 715
10.4%
2 371
 
5.4%
F 159
 
2.3%
5 145
 
2.1%
. 145
 
2.1%
Other values (8) 490
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6908
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1023
14.8%
o 973
14.1%
r 973
14.1%
y 973
14.1%
t 941
13.6%
1 715
10.4%
2 371
 
5.4%
F 159
 
2.3%
5 145
 
2.1%
. 145
 
2.1%
Other values (8) 490
7.1%

OverallQual
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1207192
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:26.073274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3891422
Coefficient of variation (CV)0.22695735
Kurtosis0.11110447
Mean6.1207192
Median Absolute Deviation (MAD)1
Skewness0.18295907
Sum7149
Variance1.929716
MonotonicityNot monotonic
2025-03-13T17:22:26.171808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 310
26.5%
6 294
25.2%
7 266
22.8%
8 133
11.4%
4 95
 
8.1%
9 38
 
3.3%
10 14
 
1.2%
3 13
 
1.1%
2 3
 
0.3%
1 2
 
0.2%
ValueCountFrequency (%)
1 2
 
0.2%
2 3
 
0.3%
3 13
 
1.1%
4 95
 
8.1%
5 310
26.5%
6 294
25.2%
7 266
22.8%
8 133
11.4%
9 38
 
3.3%
10 14
 
1.2%
ValueCountFrequency (%)
10 14
 
1.2%
9 38
 
3.3%
8 133
11.4%
7 266
22.8%
6 294
25.2%
5 310
26.5%
4 95
 
8.1%
3 13
 
1.1%
2 3
 
0.3%
1 2
 
0.2%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5599315
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:26.269318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1161373
Coefficient of variation (CV)0.20074659
Kurtosis1.2146732
Mean5.5599315
Median Absolute Deviation (MAD)0
Skewness0.6919471
Sum6494
Variance1.2457625
MonotonicityNot monotonic
2025-03-13T17:22:26.375824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 675
57.8%
6 189
 
16.2%
7 159
 
13.6%
8 60
 
5.1%
4 42
 
3.6%
3 20
 
1.7%
9 17
 
1.5%
2 5
 
0.4%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.4%
3 20
 
1.7%
4 42
 
3.6%
5 675
57.8%
6 189
 
16.2%
7 159
 
13.6%
8 60
 
5.1%
9 17
 
1.5%
ValueCountFrequency (%)
9 17
 
1.5%
8 60
 
5.1%
7 159
 
13.6%
6 189
 
16.2%
5 675
57.8%
4 42
 
3.6%
3 20
 
1.7%
2 5
 
0.4%
1 1
 
0.1%

YearRemodAdd
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1985.5248
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:26.494766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11968
median1995
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.363014
Coefficient of variation (CV)0.010255734
Kurtosis-1.1915191
Mean1985.5248
Median Absolute Deviation (MAD)12
Skewness-0.55695638
Sum2319093
Variance414.65234
MonotonicityNot monotonic
2025-03-13T17:22:26.632353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 136
 
11.6%
2006 76
 
6.5%
2007 64
 
5.5%
2005 59
 
5.1%
2004 51
 
4.4%
2003 46
 
3.9%
2000 45
 
3.9%
2002 41
 
3.5%
2008 31
 
2.7%
1998 31
 
2.7%
Other values (51) 588
50.3%
ValueCountFrequency (%)
1950 136
11.6%
1951 2
 
0.2%
1952 3
 
0.3%
1953 6
 
0.5%
1954 11
 
0.9%
1955 6
 
0.5%
1956 7
 
0.6%
1957 5
 
0.4%
1958 11
 
0.9%
1959 15
 
1.3%
ValueCountFrequency (%)
2010 6
 
0.5%
2009 17
 
1.5%
2008 31
2.7%
2007 64
5.5%
2006 76
6.5%
2005 59
5.1%
2004 51
4.4%
2003 46
3.9%
2002 41
3.5%
2001 18
 
1.5%

RoofStyle
Categorical

Imbalance 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Gable
911 
Hip
235 
Flat
 
8
Gambrel
 
6
Mansard
 
6

Length

Max length7
Median length5
Mean length4.609589
Min length3

Characters and Unicode

Total characters5384
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowHip
3rd rowGable
4th rowHip
5th rowHip

Common Values

ValueCountFrequency (%)
Gable 911
78.0%
Hip 235
 
20.1%
Flat 8
 
0.7%
Gambrel 6
 
0.5%
Mansard 6
 
0.5%
Shed 2
 
0.2%

Length

2025-03-13T17:22:26.769863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:26.883949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gable 911
78.0%
hip 235
 
20.1%
flat 8
 
0.7%
gambrel 6
 
0.5%
mansard 6
 
0.5%
shed 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 937
17.4%
l 925
17.2%
e 919
17.1%
G 917
17.0%
b 917
17.0%
H 235
 
4.4%
i 235
 
4.4%
p 235
 
4.4%
r 12
 
0.2%
d 8
 
0.1%
Other values (8) 44
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5384
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 937
17.4%
l 925
17.2%
e 919
17.1%
G 917
17.0%
b 917
17.0%
H 235
 
4.4%
i 235
 
4.4%
p 235
 
4.4%
r 12
 
0.2%
d 8
 
0.1%
Other values (8) 44
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5384
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 937
17.4%
l 925
17.2%
e 919
17.1%
G 917
17.0%
b 917
17.0%
H 235
 
4.4%
i 235
 
4.4%
p 235
 
4.4%
r 12
 
0.2%
d 8
 
0.1%
Other values (8) 44
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5384
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 937
17.4%
l 925
17.2%
e 919
17.1%
G 917
17.0%
b 917
17.0%
H 235
 
4.4%
i 235
 
4.4%
p 235
 
4.4%
r 12
 
0.2%
d 8
 
0.1%
Other values (8) 44
 
0.8%

RoofMatl
Categorical

Imbalance 

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
CompShg
1150 
Tar&Grv
 
6
WdShake
 
5
WdShngl
 
4
Membran
 
1
Other values (2)
 
2

Length

Max length7
Median length7
Mean length6.9957192
Min length4

Characters and Unicode

Total characters8171
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg

Common Values

ValueCountFrequency (%)
CompShg 1150
98.5%
Tar&Grv 6
 
0.5%
WdShake 5
 
0.4%
WdShngl 4
 
0.3%
Membran 1
 
0.1%
Metal 1
 
0.1%
Roll 1
 
0.1%

Length

2025-03-13T17:22:27.006811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:27.120823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
compshg 1150
98.5%
tar&grv 6
 
0.5%
wdshake 5
 
0.4%
wdshngl 4
 
0.3%
membran 1
 
0.1%
metal 1
 
0.1%
roll 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 1159
14.2%
h 1159
14.2%
g 1154
14.1%
m 1151
14.1%
o 1151
14.1%
C 1150
14.1%
p 1150
14.1%
a 13
 
0.2%
r 13
 
0.2%
W 9
 
0.1%
Other values (13) 62
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1159
14.2%
h 1159
14.2%
g 1154
14.1%
m 1151
14.1%
o 1151
14.1%
C 1150
14.1%
p 1150
14.1%
a 13
 
0.2%
r 13
 
0.2%
W 9
 
0.1%
Other values (13) 62
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1159
14.2%
h 1159
14.2%
g 1154
14.1%
m 1151
14.1%
o 1151
14.1%
C 1150
14.1%
p 1150
14.1%
a 13
 
0.2%
r 13
 
0.2%
W 9
 
0.1%
Other values (13) 62
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1159
14.2%
h 1159
14.2%
g 1154
14.1%
m 1151
14.1%
o 1151
14.1%
C 1150
14.1%
p 1150
14.1%
a 13
 
0.2%
r 13
 
0.2%
W 9
 
0.1%
Other values (13) 62
 
0.8%

Exterior1st
Categorical

High correlation 

Distinct15
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
VinylSd
423 
MetalSd
178 
HdBoard
176 
Wd Sdng
154 
Plywood
91 
Other values (10)
146 

Length

Max length7
Median length7
Mean length6.9803082
Min length5

Characters and Unicode

Total characters8153
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowVinylSd
2nd rowWd Sdng
3rd rowAsbShng
4th rowPlywood
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 423
36.2%
MetalSd 178
15.2%
HdBoard 176
15.1%
Wd Sdng 154
 
13.2%
Plywood 91
 
7.8%
CemntBd 47
 
4.0%
BrkFace 38
 
3.3%
WdShing 22
 
1.9%
Stucco 18
 
1.5%
AsbShng 15
 
1.3%
Other values (5) 6
 
0.5%

Length

2025-03-13T17:22:27.254986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 423
32.0%
metalsd 178
13.5%
hdboard 176
13.3%
wd 154
 
11.6%
sdng 154
 
11.6%
plywood 91
 
6.9%
cemntbd 47
 
3.6%
brkface 38
 
2.9%
wdshing 22
 
1.7%
stucco 18
 
1.4%
Other values (6) 21
 
1.6%

Most occurring characters

ValueCountFrequency (%)
d 1421
17.4%
S 814
 
10.0%
l 693
 
8.5%
n 664
 
8.1%
y 514
 
6.3%
i 445
 
5.5%
V 423
 
5.2%
a 392
 
4.8%
o 380
 
4.7%
e 265
 
3.3%
Other values (22) 2142
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1421
17.4%
S 814
 
10.0%
l 693
 
8.5%
n 664
 
8.1%
y 514
 
6.3%
i 445
 
5.5%
V 423
 
5.2%
a 392
 
4.8%
o 380
 
4.7%
e 265
 
3.3%
Other values (22) 2142
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1421
17.4%
S 814
 
10.0%
l 693
 
8.5%
n 664
 
8.1%
y 514
 
6.3%
i 445
 
5.5%
V 423
 
5.2%
a 392
 
4.8%
o 380
 
4.7%
e 265
 
3.3%
Other values (22) 2142
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1421
17.4%
S 814
 
10.0%
l 693
 
8.5%
n 664
 
8.1%
y 514
 
6.3%
i 445
 
5.5%
V 423
 
5.2%
a 392
 
4.8%
o 380
 
4.7%
e 265
 
3.3%
Other values (22) 2142
26.3%

Exterior2nd
Categorical

High correlation 

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
VinylSd
414 
MetalSd
174 
HdBoard
164 
Wd Sdng
148 
Plywood
118 
Other values (11)
150 

Length

Max length7
Median length7
Mean length6.9726027
Min length5

Characters and Unicode

Total characters8144
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowWd Sdng
3rd rowAsbShng
4th rowHdBoard
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 414
35.4%
MetalSd 174
14.9%
HdBoard 164
 
14.0%
Wd Sdng 148
 
12.7%
Plywood 118
 
10.1%
CmentBd 46
 
3.9%
Wd Shng 31
 
2.7%
Stucco 19
 
1.6%
BrkFace 18
 
1.5%
AsbShng 14
 
1.2%
Other values (6) 22
 
1.9%

Length

2025-03-13T17:22:27.392437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 414
30.6%
wd 179
13.2%
metalsd 174
12.9%
hdboard 164
 
12.1%
sdng 148
 
10.9%
plywood 118
 
8.7%
cmentbd 46
 
3.4%
shng 31
 
2.3%
stucco 19
 
1.4%
brkface 18
 
1.3%
Other values (8) 41
 
3.0%

Most occurring characters

ValueCountFrequency (%)
d 1407
17.3%
S 815
 
10.0%
l 707
 
8.7%
n 665
 
8.2%
y 532
 
6.5%
o 425
 
5.2%
V 414
 
5.1%
i 414
 
5.1%
a 356
 
4.4%
t 253
 
3.1%
Other values (23) 2156
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1407
17.3%
S 815
 
10.0%
l 707
 
8.7%
n 665
 
8.2%
y 532
 
6.5%
o 425
 
5.2%
V 414
 
5.1%
i 414
 
5.1%
a 356
 
4.4%
t 253
 
3.1%
Other values (23) 2156
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1407
17.3%
S 815
 
10.0%
l 707
 
8.7%
n 665
 
8.2%
y 532
 
6.5%
o 425
 
5.2%
V 414
 
5.1%
i 414
 
5.1%
a 356
 
4.4%
t 253
 
3.1%
Other values (23) 2156
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1407
17.3%
S 815
 
10.0%
l 707
 
8.7%
n 665
 
8.2%
y 532
 
6.5%
o 425
 
5.2%
V 414
 
5.1%
i 414
 
5.1%
a 356
 
4.4%
t 253
 
3.1%
Other values (23) 2156
26.5%

MasVnrArea
Real number (ℝ)

Zeros 

Distinct297
Distinct (%)25.5%
Missing5
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean106.09458
Minimum0
Maximum1600
Zeros678
Zeros (%)58.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:27.522951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3167.5
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)167.5

Descriptive statistics

Standard deviation182.79412
Coefficient of variation (CV)1.7229355
Kurtosis9.6966818
Mean106.09458
Median Absolute Deviation (MAD)0
Skewness2.6319161
Sum123388
Variance33413.69
MonotonicityNot monotonic
2025-03-13T17:22:27.666884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 678
58.0%
108 7
 
0.6%
72 6
 
0.5%
180 6
 
0.5%
200 6
 
0.5%
80 5
 
0.4%
106 5
 
0.4%
16 5
 
0.4%
120 5
 
0.4%
100 4
 
0.3%
Other values (287) 436
37.3%
(Missing) 5
 
0.4%
ValueCountFrequency (%)
0 678
58.0%
1 1
 
0.1%
11 1
 
0.1%
16 5
 
0.4%
18 1
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
30 2
 
0.2%
ValueCountFrequency (%)
1600 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%
894 1
0.1%

ExterQual
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
TA
709 
Gd
404 
Ex
 
43
Fa
 
12

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2336
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowTA
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 709
60.7%
Gd 404
34.6%
Ex 43
 
3.7%
Fa 12
 
1.0%

Length

2025-03-13T17:22:27.801649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:27.898364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 709
60.7%
gd 404
34.6%
ex 43
 
3.7%
fa 12
 
1.0%

Most occurring characters

ValueCountFrequency (%)
T 709
30.4%
A 709
30.4%
G 404
17.3%
d 404
17.3%
E 43
 
1.8%
x 43
 
1.8%
F 12
 
0.5%
a 12
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 709
30.4%
A 709
30.4%
G 404
17.3%
d 404
17.3%
E 43
 
1.8%
x 43
 
1.8%
F 12
 
0.5%
a 12
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 709
30.4%
A 709
30.4%
G 404
17.3%
d 404
17.3%
E 43
 
1.8%
x 43
 
1.8%
F 12
 
0.5%
a 12
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 709
30.4%
A 709
30.4%
G 404
17.3%
d 404
17.3%
E 43
 
1.8%
x 43
 
1.8%
F 12
 
0.5%
a 12
 
0.5%

ExterCond
Categorical

Imbalance 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
TA
1026 
Gd
114 
Fa
 
25
Ex
 
2
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2336
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1026
87.8%
Gd 114
 
9.8%
Fa 25
 
2.1%
Ex 2
 
0.2%
Po 1
 
0.1%

Length

2025-03-13T17:22:28.007998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:28.105129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 1026
87.8%
gd 114
 
9.8%
fa 25
 
2.1%
ex 2
 
0.2%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1026
43.9%
A 1026
43.9%
G 114
 
4.9%
d 114
 
4.9%
F 25
 
1.1%
a 25
 
1.1%
E 2
 
0.1%
x 2
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1026
43.9%
A 1026
43.9%
G 114
 
4.9%
d 114
 
4.9%
F 25
 
1.1%
a 25
 
1.1%
E 2
 
0.1%
x 2
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1026
43.9%
A 1026
43.9%
G 114
 
4.9%
d 114
 
4.9%
F 25
 
1.1%
a 25
 
1.1%
E 2
 
0.1%
x 2
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1026
43.9%
A 1026
43.9%
G 114
 
4.9%
d 114
 
4.9%
F 25
 
1.1%
a 25
 
1.1%
E 2
 
0.1%
x 2
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Foundation
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
PConc
531 
CBlock
505 
BrkTil
105 
Slab
 
19
Stone
 
5

Length

Max length6
Median length6
Mean length5.5034247
Min length4

Characters and Unicode

Total characters6428
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowCBlock
4th rowPConc
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc 531
45.5%
CBlock 505
43.2%
BrkTil 105
 
9.0%
Slab 19
 
1.6%
Stone 5
 
0.4%
Wood 3
 
0.3%

Length

2025-03-13T17:22:28.232946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:28.349953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pconc 531
45.5%
cblock 505
43.2%
brktil 105
 
9.0%
slab 19
 
1.6%
stone 5
 
0.4%
wood 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o 1047
16.3%
C 1036
16.1%
c 1036
16.1%
l 629
9.8%
B 610
9.5%
k 610
9.5%
n 536
8.3%
P 531
8.3%
i 105
 
1.6%
T 105
 
1.6%
Other values (8) 183
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1047
16.3%
C 1036
16.1%
c 1036
16.1%
l 629
9.8%
B 610
9.5%
k 610
9.5%
n 536
8.3%
P 531
8.3%
i 105
 
1.6%
T 105
 
1.6%
Other values (8) 183
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1047
16.3%
C 1036
16.1%
c 1036
16.1%
l 629
9.8%
B 610
9.5%
k 610
9.5%
n 536
8.3%
P 531
8.3%
i 105
 
1.6%
T 105
 
1.6%
Other values (8) 183
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1047
16.3%
C 1036
16.1%
c 1036
16.1%
l 629
9.8%
B 610
9.5%
k 610
9.5%
n 536
8.3%
P 531
8.3%
i 105
 
1.6%
T 105
 
1.6%
Other values (8) 183
 
2.8%

BsmtQual
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
TA
537 
Gd
505 
Ex
104 
Fa
 
22

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2336
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEx
2nd rowTA
3rd rowTA
4th rowTA
5th rowEx

Common Values

ValueCountFrequency (%)
TA 537
46.0%
Gd 505
43.2%
Ex 104
 
8.9%
Fa 22
 
1.9%

Length

2025-03-13T17:22:28.469963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:28.563981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 537
46.0%
gd 505
43.2%
ex 104
 
8.9%
fa 22
 
1.9%

Most occurring characters

ValueCountFrequency (%)
T 537
23.0%
A 537
23.0%
G 505
21.6%
d 505
21.6%
E 104
 
4.5%
x 104
 
4.5%
F 22
 
0.9%
a 22
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 537
23.0%
A 537
23.0%
G 505
21.6%
d 505
21.6%
E 104
 
4.5%
x 104
 
4.5%
F 22
 
0.9%
a 22
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 537
23.0%
A 537
23.0%
G 505
21.6%
d 505
21.6%
E 104
 
4.5%
x 104
 
4.5%
F 22
 
0.9%
a 22
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 537
23.0%
A 537
23.0%
G 505
21.6%
d 505
21.6%
E 104
 
4.5%
x 104
 
4.5%
F 22
 
0.9%
a 22
 
0.9%

BsmtCond
Categorical

Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
TA
1083 
Gd
 
48
Fa
 
35
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2336
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1083
92.7%
Gd 48
 
4.1%
Fa 35
 
3.0%
Po 2
 
0.2%

Length

2025-03-13T17:22:28.670005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:28.761261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 1083
92.7%
gd 48
 
4.1%
fa 35
 
3.0%
po 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 1083
46.4%
A 1083
46.4%
G 48
 
2.1%
d 48
 
2.1%
F 35
 
1.5%
a 35
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1083
46.4%
A 1083
46.4%
G 48
 
2.1%
d 48
 
2.1%
F 35
 
1.5%
a 35
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1083
46.4%
A 1083
46.4%
G 48
 
2.1%
d 48
 
2.1%
F 35
 
1.5%
a 35
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1083
46.4%
A 1083
46.4%
G 48
 
2.1%
d 48
 
2.1%
F 35
 
1.5%
a 35
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

BsmtExposure
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
No
790 
Av
186 
Gd
105 
Mn
87 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2336
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMn
2nd rowNo
3rd rowNo
4th rowMn
5th rowGd

Common Values

ValueCountFrequency (%)
No 790
67.6%
Av 186
 
15.9%
Gd 105
 
9.0%
Mn 87
 
7.4%

Length

2025-03-13T17:22:28.860518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:28.952534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 790
67.6%
av 186
 
15.9%
gd 105
 
9.0%
mn 87
 
7.4%

Most occurring characters

ValueCountFrequency (%)
N 790
33.8%
o 790
33.8%
A 186
 
8.0%
v 186
 
8.0%
G 105
 
4.5%
d 105
 
4.5%
M 87
 
3.7%
n 87
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 790
33.8%
o 790
33.8%
A 186
 
8.0%
v 186
 
8.0%
G 105
 
4.5%
d 105
 
4.5%
M 87
 
3.7%
n 87
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 790
33.8%
o 790
33.8%
A 186
 
8.0%
v 186
 
8.0%
G 105
 
4.5%
d 105
 
4.5%
M 87
 
3.7%
n 87
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 790
33.8%
o 790
33.8%
A 186
 
8.0%
v 186
 
8.0%
G 105
 
4.5%
d 105
 
4.5%
M 87
 
3.7%
n 87
 
3.7%

BsmtFinType1
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Unf
384 
GLQ
331 
ALQ
176 
BLQ
114 
Rec
101 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3504
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowUnf
3rd rowUnf
4th rowBLQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf 384
32.9%
GLQ 331
28.3%
ALQ 176
15.1%
BLQ 114
 
9.8%
Rec 101
 
8.6%
LwQ 62
 
5.3%

Length

2025-03-13T17:22:29.060030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:29.165037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unf 384
32.9%
glq 331
28.3%
alq 176
15.1%
blq 114
 
9.8%
rec 101
 
8.6%
lwq 62
 
5.3%

Most occurring characters

ValueCountFrequency (%)
L 683
19.5%
Q 683
19.5%
U 384
11.0%
n 384
11.0%
f 384
11.0%
G 331
9.4%
A 176
 
5.0%
B 114
 
3.3%
R 101
 
2.9%
e 101
 
2.9%
Other values (2) 163
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 683
19.5%
Q 683
19.5%
U 384
11.0%
n 384
11.0%
f 384
11.0%
G 331
9.4%
A 176
 
5.0%
B 114
 
3.3%
R 101
 
2.9%
e 101
 
2.9%
Other values (2) 163
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 683
19.5%
Q 683
19.5%
U 384
11.0%
n 384
11.0%
f 384
11.0%
G 331
9.4%
A 176
 
5.0%
B 114
 
3.3%
R 101
 
2.9%
e 101
 
2.9%
Other values (2) 163
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 683
19.5%
Q 683
19.5%
U 384
11.0%
n 384
11.0%
f 384
11.0%
G 331
9.4%
A 176
 
5.0%
B 114
 
3.3%
R 101
 
2.9%
e 101
 
2.9%
Other values (2) 163
 
4.7%

BsmtFinSF1
Real number (ℝ)

High correlation  Zeros 

Distinct541
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean436.7714
Minimum0
Maximum2260
Zeros384
Zeros (%)32.9%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:29.300630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median377.5
Q3706
95-th percentile1274
Maximum2260
Range2260
Interquartile range (IQR)706

Descriptive statistics

Standard deviation440.00448
Coefficient of variation (CV)1.0074022
Kurtosis0.1012883
Mean436.7714
Median Absolute Deviation (MAD)377.5
Skewness0.8295884
Sum510149
Variance193603.94
MonotonicityNot monotonic
2025-03-13T17:22:29.430651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
32.9%
24 10
 
0.9%
16 5
 
0.4%
504 4
 
0.3%
662 4
 
0.3%
641 4
 
0.3%
384 4
 
0.3%
360 4
 
0.3%
600 4
 
0.3%
560 4
 
0.3%
Other values (531) 741
63.4%
ValueCountFrequency (%)
0 384
32.9%
2 1
 
0.1%
16 5
 
0.4%
20 3
 
0.3%
24 10
 
0.9%
25 1
 
0.1%
27 1
 
0.1%
28 2
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%
1646 1
0.1%
1636 1
0.1%

BsmtFinType2
Categorical

Imbalance 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Unf
1029 
Rec
 
46
LwQ
 
36
BLQ
 
27
ALQ
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3504
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf

Common Values

ValueCountFrequency (%)
Unf 1029
88.1%
Rec 46
 
3.9%
LwQ 36
 
3.1%
BLQ 27
 
2.3%
ALQ 16
 
1.4%
GLQ 14
 
1.2%

Length

2025-03-13T17:22:29.548083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:29.650095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unf 1029
88.1%
rec 46
 
3.9%
lwq 36
 
3.1%
blq 27
 
2.3%
alq 16
 
1.4%
glq 14
 
1.2%

Most occurring characters

ValueCountFrequency (%)
U 1029
29.4%
n 1029
29.4%
f 1029
29.4%
L 93
 
2.7%
Q 93
 
2.7%
R 46
 
1.3%
e 46
 
1.3%
c 46
 
1.3%
w 36
 
1.0%
B 27
 
0.8%
Other values (2) 30
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1029
29.4%
n 1029
29.4%
f 1029
29.4%
L 93
 
2.7%
Q 93
 
2.7%
R 46
 
1.3%
e 46
 
1.3%
c 46
 
1.3%
w 36
 
1.0%
B 27
 
0.8%
Other values (2) 30
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1029
29.4%
n 1029
29.4%
f 1029
29.4%
L 93
 
2.7%
Q 93
 
2.7%
R 46
 
1.3%
e 46
 
1.3%
c 46
 
1.3%
w 36
 
1.0%
B 27
 
0.8%
Other values (2) 30
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1029
29.4%
n 1029
29.4%
f 1029
29.4%
L 93
 
2.7%
Q 93
 
2.7%
R 46
 
1.3%
e 46
 
1.3%
c 46
 
1.3%
w 36
 
1.0%
B 27
 
0.8%
Other values (2) 30
 
0.9%

BsmtUnfSF
Real number (ℝ)

High correlation  Zeros 

Distinct680
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean578.84247
Minimum0
Maximum2336
Zeros95
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:29.769100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1237.75
median493
Q3816.5
95-th percentile1489
Maximum2336
Range2336
Interquartile range (IQR)578.75

Descriptive statistics

Standard deviation446.34566
Coefficient of variation (CV)0.77110041
Kurtosis0.4098193
Mean578.84247
Median Absolute Deviation (MAD)292.5
Skewness0.89256904
Sum676088
Variance199224.45
MonotonicityNot monotonic
2025-03-13T17:22:29.900210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95
 
8.1%
728 7
 
0.6%
384 6
 
0.5%
600 6
 
0.5%
300 6
 
0.5%
440 6
 
0.5%
625 5
 
0.4%
572 5
 
0.4%
264 5
 
0.4%
270 5
 
0.4%
Other values (670) 1022
87.5%
ValueCountFrequency (%)
0 95
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 1
 
0.1%
26 1
 
0.1%
30 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
38 1
 
0.1%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2002 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%
1905 1
0.1%
1869 1
0.1%

Heating
Categorical

Imbalance 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
GasA
1148 
GasW
 
10
Grav
 
6
Wall
 
3
Floor
 
1

Length

Max length5
Median length4
Mean length4.0008562
Min length4

Characters and Unicode

Total characters4673
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA 1148
98.3%
GasW 10
 
0.9%
Grav 6
 
0.5%
Wall 3
 
0.3%
Floor 1
 
0.1%

Length

2025-03-13T17:22:30.022213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:30.122232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gasa 1148
98.3%
gasw 10
 
0.9%
grav 6
 
0.5%
wall 3
 
0.3%
floor 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1167
25.0%
G 1164
24.9%
s 1158
24.8%
A 1148
24.6%
W 13
 
0.3%
r 7
 
0.1%
l 7
 
0.1%
v 6
 
0.1%
o 2
 
< 0.1%
F 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1167
25.0%
G 1164
24.9%
s 1158
24.8%
A 1148
24.6%
W 13
 
0.3%
r 7
 
0.1%
l 7
 
0.1%
v 6
 
0.1%
o 2
 
< 0.1%
F 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1167
25.0%
G 1164
24.9%
s 1158
24.8%
A 1148
24.6%
W 13
 
0.3%
r 7
 
0.1%
l 7
 
0.1%
v 6
 
0.1%
o 2
 
< 0.1%
F 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1167
25.0%
G 1164
24.9%
s 1158
24.8%
A 1148
24.6%
W 13
 
0.3%
r 7
 
0.1%
l 7
 
0.1%
v 6
 
0.1%
o 2
 
< 0.1%
F 1
 
< 0.1%

HeatingQC
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Ex
602 
TA
340 
Gd
185 
Fa
 
40
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2336
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowTA
3rd rowEx
4th rowEx
5th rowEx

Common Values

ValueCountFrequency (%)
Ex 602
51.5%
TA 340
29.1%
Gd 185
 
15.8%
Fa 40
 
3.4%
Po 1
 
0.1%

Length

2025-03-13T17:22:30.230151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:30.326158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ex 602
51.5%
ta 340
29.1%
gd 185
 
15.8%
fa 40
 
3.4%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 602
25.8%
x 602
25.8%
T 340
14.6%
A 340
14.6%
G 185
 
7.9%
d 185
 
7.9%
F 40
 
1.7%
a 40
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 602
25.8%
x 602
25.8%
T 340
14.6%
A 340
14.6%
G 185
 
7.9%
d 185
 
7.9%
F 40
 
1.7%
a 40
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 602
25.8%
x 602
25.8%
T 340
14.6%
A 340
14.6%
G 185
 
7.9%
d 185
 
7.9%
F 40
 
1.7%
a 40
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 602
25.8%
x 602
25.8%
T 340
14.6%
A 340
14.6%
G 185
 
7.9%
d 185
 
7.9%
F 40
 
1.7%
a 40
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

CentralAir
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
True
1096 
False
 
72
ValueCountFrequency (%)
True 1096
93.8%
False 72
 
6.2%
2025-03-13T17:22:30.420169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Electrical
Categorical

Imbalance 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
SBrkr
1071 
FuseA
 
74
FuseF
 
20
FuseP
 
2
Mix
 
1

Length

Max length5
Median length5
Mean length4.9982877
Min length3

Characters and Unicode

Total characters5838
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowFuseA
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr 1071
91.7%
FuseA 74
 
6.3%
FuseF 20
 
1.7%
FuseP 2
 
0.2%
Mix 1
 
0.1%

Length

2025-03-13T17:22:30.528261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:30.636192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr 1071
91.7%
fusea 74
 
6.3%
fusef 20
 
1.7%
fusep 2
 
0.2%
mix 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 2142
36.7%
S 1071
18.3%
B 1071
18.3%
k 1071
18.3%
F 116
 
2.0%
u 96
 
1.6%
s 96
 
1.6%
e 96
 
1.6%
A 74
 
1.3%
P 2
 
< 0.1%
Other values (3) 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2142
36.7%
S 1071
18.3%
B 1071
18.3%
k 1071
18.3%
F 116
 
2.0%
u 96
 
1.6%
s 96
 
1.6%
e 96
 
1.6%
A 74
 
1.3%
P 2
 
< 0.1%
Other values (3) 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2142
36.7%
S 1071
18.3%
B 1071
18.3%
k 1071
18.3%
F 116
 
2.0%
u 96
 
1.6%
s 96
 
1.6%
e 96
 
1.6%
A 74
 
1.3%
P 2
 
< 0.1%
Other values (3) 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2142
36.7%
S 1071
18.3%
B 1071
18.3%
k 1071
18.3%
F 116
 
2.0%
u 96
 
1.6%
s 96
 
1.6%
e 96
 
1.6%
A 74
 
1.3%
P 2
 
< 0.1%
Other values (3) 3
 
0.1%

2ndFlrSF
Real number (ℝ)

Zeros 

Distinct350
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.2286
Minimum0
Maximum2065
Zeros671
Zeros (%)57.4%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:30.754358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.65
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.67823
Coefficient of variation (CV)1.275984
Kurtosis-0.4901961
Mean342.2286
Median Absolute Deviation (MAD)0
Skewness0.84399281
Sum399723
Variance190687.87
MonotonicityNot monotonic
2025-03-13T17:22:30.884218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 671
57.4%
728 9
 
0.8%
546 8
 
0.7%
600 7
 
0.6%
720 6
 
0.5%
896 6
 
0.5%
840 5
 
0.4%
672 5
 
0.4%
756 5
 
0.4%
551 4
 
0.3%
Other values (340) 442
37.8%
ValueCountFrequency (%)
0 671
57.4%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 1
 
0.1%
252 2
 
0.2%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1611 1
0.1%
1589 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%
1479 1
0.1%
1440 1
0.1%

BsmtFullBath
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
0
677 
1
478 
2
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1168
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 677
58.0%
1 478
40.9%
2 13
 
1.1%

Length

2025-03-13T17:22:30.998386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:31.087247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 677
58.0%
1 478
40.9%
2 13
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 677
58.0%
1 478
40.9%
2 13
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 677
58.0%
1 478
40.9%
2 13
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 677
58.0%
1 478
40.9%
2 13
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 677
58.0%
1 478
40.9%
2 13
 
1.1%

FullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2
624 
1
508 
3
 
29
0
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1168
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 624
53.4%
1 508
43.5%
3 29
 
2.5%
0 7
 
0.6%

Length

2025-03-13T17:22:31.188251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:31.282283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 624
53.4%
1 508
43.5%
3 29
 
2.5%
0 7
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 624
53.4%
1 508
43.5%
3 29
 
2.5%
0 7
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 624
53.4%
1 508
43.5%
3 29
 
2.5%
0 7
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 624
53.4%
1 508
43.5%
3 29
 
2.5%
0 7
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 624
53.4%
1 508
43.5%
3 29
 
2.5%
0 7
 
0.6%

HalfBath
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
0
727 
1
431 
2
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1168
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 727
62.2%
1 431
36.9%
2 10
 
0.9%

Length

2025-03-13T17:22:31.385268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:31.475278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 727
62.2%
1 431
36.9%
2 10
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 727
62.2%
1 431
36.9%
2 10
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 727
62.2%
1 431
36.9%
2 10
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 727
62.2%
1 431
36.9%
2 10
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 727
62.2%
1 431
36.9%
2 10
 
0.9%

BedroomAbvGr
Real number (ℝ)

Distinct8
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.864726
Minimum0
Maximum8
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:32.121348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81156313
Coefficient of variation (CV)0.2832952
Kurtosis2.5205991
Mean2.864726
Median Absolute Deviation (MAD)0
Skewness0.22291977
Sum3346
Variance0.65863471
MonotonicityNot monotonic
2025-03-13T17:22:32.224938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 653
55.9%
2 282
24.1%
4 165
 
14.1%
1 40
 
3.4%
5 17
 
1.5%
0 5
 
0.4%
6 5
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 5
 
0.4%
1 40
 
3.4%
2 282
24.1%
3 653
55.9%
4 165
 
14.1%
5 17
 
1.5%
6 5
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 5
 
0.4%
5 17
 
1.5%
4 165
 
14.1%
3 653
55.9%
2 282
24.1%
1 40
 
3.4%
0 5
 
0.4%

KitchenAbvGr
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
1
1110 
2
 
57
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1168
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1110
95.0%
2 57
 
4.9%
3 1
 
0.1%

Length

2025-03-13T17:22:32.341387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:32.428387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1110
95.0%
2 57
 
4.9%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1110
95.0%
2 57
 
4.9%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1110
95.0%
2 57
 
4.9%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1110
95.0%
2 57
 
4.9%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1110
95.0%
2 57
 
4.9%
3 1
 
0.1%

KitchenQual
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
TA
577 
Gd
478 
Ex
83 
Fa
 
30

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2336
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEx
2nd rowTA
3rd rowTA
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 577
49.4%
Gd 478
40.9%
Ex 83
 
7.1%
Fa 30
 
2.6%

Length

2025-03-13T17:22:32.523971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:32.617396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 577
49.4%
gd 478
40.9%
ex 83
 
7.1%
fa 30
 
2.6%

Most occurring characters

ValueCountFrequency (%)
T 577
24.7%
A 577
24.7%
G 478
20.5%
d 478
20.5%
E 83
 
3.6%
x 83
 
3.6%
F 30
 
1.3%
a 30
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 577
24.7%
A 577
24.7%
G 478
20.5%
d 478
20.5%
E 83
 
3.6%
x 83
 
3.6%
F 30
 
1.3%
a 30
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 577
24.7%
A 577
24.7%
G 478
20.5%
d 478
20.5%
E 83
 
3.6%
x 83
 
3.6%
F 30
 
1.3%
a 30
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 577
24.7%
A 577
24.7%
G 478
20.5%
d 478
20.5%
E 83
 
3.6%
x 83
 
3.6%
F 30
 
1.3%
a 30
 
1.3%

Functional
Categorical

Imbalance 

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Typ
1081 
Min2
 
29
Min1
 
25
Mod
 
15
Maj1
 
12
Other values (2)
 
6

Length

Max length4
Median length3
Mean length3.0607877
Min length3

Characters and Unicode

Total characters3575
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp

Common Values

ValueCountFrequency (%)
Typ 1081
92.6%
Min2 29
 
2.5%
Min1 25
 
2.1%
Mod 15
 
1.3%
Maj1 12
 
1.0%
Maj2 5
 
0.4%
Sev 1
 
0.1%

Length

2025-03-13T17:22:32.725410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:32.829419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
typ 1081
92.6%
min2 29
 
2.5%
min1 25
 
2.1%
mod 15
 
1.3%
maj1 12
 
1.0%
maj2 5
 
0.4%
sev 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1081
30.2%
y 1081
30.2%
p 1081
30.2%
M 86
 
2.4%
i 54
 
1.5%
n 54
 
1.5%
1 37
 
1.0%
2 34
 
1.0%
a 17
 
0.5%
j 17
 
0.5%
Other values (5) 33
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1081
30.2%
y 1081
30.2%
p 1081
30.2%
M 86
 
2.4%
i 54
 
1.5%
n 54
 
1.5%
1 37
 
1.0%
2 34
 
1.0%
a 17
 
0.5%
j 17
 
0.5%
Other values (5) 33
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1081
30.2%
y 1081
30.2%
p 1081
30.2%
M 86
 
2.4%
i 54
 
1.5%
n 54
 
1.5%
1 37
 
1.0%
2 34
 
1.0%
a 17
 
0.5%
j 17
 
0.5%
Other values (5) 33
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1081
30.2%
y 1081
30.2%
p 1081
30.2%
M 86
 
2.4%
i 54
 
1.5%
n 54
 
1.5%
1 37
 
1.0%
2 34
 
1.0%
a 17
 
0.5%
j 17
 
0.5%
Other values (5) 33
 
0.9%

Fireplaces
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
0
553 
1
519 
2
94 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1168
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 553
47.3%
1 519
44.4%
2 94
 
8.0%
3 2
 
0.2%

Length

2025-03-13T17:22:32.942432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:33.034586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 553
47.3%
1 519
44.4%
2 94
 
8.0%
3 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 553
47.3%
1 519
44.4%
2 94
 
8.0%
3 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 553
47.3%
1 519
44.4%
2 94
 
8.0%
3 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 553
47.3%
1 519
44.4%
2 94
 
8.0%
3 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 553
47.3%
1 519
44.4%
2 94
 
8.0%
3 2
 
0.2%

GarageType
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Attchd
767 
Detchd
302 
BuiltIn
 
72
Basment
 
15
CarPort
 
7

Length

Max length7
Median length6
Mean length6.0804795
Min length6

Characters and Unicode

Total characters7102
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowBasment
4th rowAttchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd 767
65.7%
Detchd 302
 
25.9%
BuiltIn 72
 
6.2%
Basment 15
 
1.3%
CarPort 7
 
0.6%
2Types 5
 
0.4%

Length

2025-03-13T17:22:33.139514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:33.240735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
attchd 767
65.7%
detchd 302
 
25.9%
builtin 72
 
6.2%
basment 15
 
1.3%
carport 7
 
0.6%
2types 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 1930
27.2%
c 1069
15.1%
h 1069
15.1%
d 1069
15.1%
A 767
 
10.8%
e 322
 
4.5%
D 302
 
4.3%
n 87
 
1.2%
B 87
 
1.2%
u 72
 
1.0%
Other values (14) 328
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1930
27.2%
c 1069
15.1%
h 1069
15.1%
d 1069
15.1%
A 767
 
10.8%
e 322
 
4.5%
D 302
 
4.3%
n 87
 
1.2%
B 87
 
1.2%
u 72
 
1.0%
Other values (14) 328
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1930
27.2%
c 1069
15.1%
h 1069
15.1%
d 1069
15.1%
A 767
 
10.8%
e 322
 
4.5%
D 302
 
4.3%
n 87
 
1.2%
B 87
 
1.2%
u 72
 
1.0%
Other values (14) 328
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1930
27.2%
c 1069
15.1%
h 1069
15.1%
d 1069
15.1%
A 767
 
10.8%
e 322
 
4.5%
D 302
 
4.3%
n 87
 
1.2%
B 87
 
1.2%
u 72
 
1.0%
Other values (14) 328
 
4.6%

GarageFinish
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Unf
539 
RFn
348 
Fin
281 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3504
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFin
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf 539
46.1%
RFn 348
29.8%
Fin 281
24.1%

Length

2025-03-13T17:22:33.352534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:33.441484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unf 539
46.1%
rfn 348
29.8%
fin 281
24.1%

Most occurring characters

ValueCountFrequency (%)
n 1168
33.3%
F 629
18.0%
U 539
15.4%
f 539
15.4%
R 348
 
9.9%
i 281
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1168
33.3%
F 629
18.0%
U 539
15.4%
f 539
15.4%
R 348
 
9.9%
i 281
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1168
33.3%
F 629
18.0%
U 539
15.4%
f 539
15.4%
R 348
 
9.9%
i 281
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1168
33.3%
F 629
18.0%
U 539
15.4%
f 539
15.4%
R 348
 
9.9%
i 281
 
8.0%

GarageQual
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
TA
1113 
Fa
 
39
Gd
 
12
Po
 
3
Ex
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2336
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1113
95.3%
Fa 39
 
3.3%
Gd 12
 
1.0%
Po 3
 
0.3%
Ex 1
 
0.1%

Length

2025-03-13T17:22:33.540496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:33.633523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 1113
95.3%
fa 39
 
3.3%
gd 12
 
1.0%
po 3
 
0.3%
ex 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1113
47.6%
A 1113
47.6%
F 39
 
1.7%
a 39
 
1.7%
G 12
 
0.5%
d 12
 
0.5%
P 3
 
0.1%
o 3
 
0.1%
E 1
 
< 0.1%
x 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1113
47.6%
A 1113
47.6%
F 39
 
1.7%
a 39
 
1.7%
G 12
 
0.5%
d 12
 
0.5%
P 3
 
0.1%
o 3
 
0.1%
E 1
 
< 0.1%
x 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1113
47.6%
A 1113
47.6%
F 39
 
1.7%
a 39
 
1.7%
G 12
 
0.5%
d 12
 
0.5%
P 3
 
0.1%
o 3
 
0.1%
E 1
 
< 0.1%
x 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1113
47.6%
A 1113
47.6%
F 39
 
1.7%
a 39
 
1.7%
G 12
 
0.5%
d 12
 
0.5%
P 3
 
0.1%
o 3
 
0.1%
E 1
 
< 0.1%
x 1
 
< 0.1%

GarageCond
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
TA
1125 
Fa
 
28
Gd
 
8
Po
 
6
Ex
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2336
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1125
96.3%
Fa 28
 
2.4%
Gd 8
 
0.7%
Po 6
 
0.5%
Ex 1
 
0.1%

Length

2025-03-13T17:22:33.736595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:33.831800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 1125
96.3%
fa 28
 
2.4%
gd 8
 
0.7%
po 6
 
0.5%
ex 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1125
48.2%
A 1125
48.2%
F 28
 
1.2%
a 28
 
1.2%
G 8
 
0.3%
d 8
 
0.3%
P 6
 
0.3%
o 6
 
0.3%
E 1
 
< 0.1%
x 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1125
48.2%
A 1125
48.2%
F 28
 
1.2%
a 28
 
1.2%
G 8
 
0.3%
d 8
 
0.3%
P 6
 
0.3%
o 6
 
0.3%
E 1
 
< 0.1%
x 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1125
48.2%
A 1125
48.2%
F 28
 
1.2%
a 28
 
1.2%
G 8
 
0.3%
d 8
 
0.3%
P 6
 
0.3%
o 6
 
0.3%
E 1
 
< 0.1%
x 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1125
48.2%
A 1125
48.2%
F 28
 
1.2%
a 28
 
1.2%
G 8
 
0.3%
d 8
 
0.3%
P 6
 
0.3%
o 6
 
0.3%
E 1
 
< 0.1%
x 1
 
< 0.1%

PavedDrive
Categorical

Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Y
1080 
N
 
69
P
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1168
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 1080
92.5%
N 69
 
5.9%
P 19
 
1.6%

Length

2025-03-13T17:22:33.938616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:34.028547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
y 1080
92.5%
n 69
 
5.9%
p 19
 
1.6%

Most occurring characters

ValueCountFrequency (%)
Y 1080
92.5%
N 69
 
5.9%
P 19
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 1080
92.5%
N 69
 
5.9%
P 19
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 1080
92.5%
N 69
 
5.9%
P 19
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 1080
92.5%
N 69
 
5.9%
P 19
 
1.6%

WoodDeckSF
Real number (ℝ)

Zeros 

Distinct238
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.364726
Minimum0
Maximum670
Zeros601
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:34.130560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum670
Range670
Interquartile range (IQR)168

Descriptive statistics

Standard deviation123.21521
Coefficient of variation (CV)1.2920418
Kurtosis1.7322869
Mean95.364726
Median Absolute Deviation (MAD)0
Skewness1.3476402
Sum111386
Variance15181.989
MonotonicityNot monotonic
2025-03-13T17:22:34.264644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 601
51.5%
192 31
 
2.7%
100 29
 
2.5%
144 28
 
2.4%
120 26
 
2.2%
168 24
 
2.1%
208 10
 
0.9%
140 10
 
0.9%
224 9
 
0.8%
240 9
 
0.8%
Other values (228) 391
33.5%
ValueCountFrequency (%)
0 601
51.5%
12 1
 
0.1%
24 2
 
0.2%
26 2
 
0.2%
28 2
 
0.2%
30 1
 
0.1%
32 1
 
0.1%
36 3
 
0.3%
38 1
 
0.1%
40 3
 
0.3%
ValueCountFrequency (%)
670 1
0.1%
668 1
0.1%
635 1
0.1%
576 1
0.1%
574 1
0.1%
550 1
0.1%
536 1
0.1%
519 1
0.1%
517 1
0.1%
511 1
0.1%

OpenPorchSF
Real number (ℝ)

Zeros 

Distinct187
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.842466
Minimum0
Maximum547
Zeros502
Zeros (%)43.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:34.394588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28
Q369
95-th percentile184.65
Maximum547
Range547
Interquartile range (IQR)69

Descriptive statistics

Standard deviation68.477512
Coefficient of variation (CV)1.4020077
Kurtosis8.7084018
Mean48.842466
Median Absolute Deviation (MAD)28
Skewness2.4056306
Sum57048
Variance4689.1697
MonotonicityNot monotonic
2025-03-13T17:22:34.518596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 502
43.0%
36 27
 
2.3%
45 17
 
1.5%
48 17
 
1.5%
40 15
 
1.3%
20 15
 
1.3%
24 14
 
1.2%
39 13
 
1.1%
30 13
 
1.1%
50 12
 
1.0%
Other values (177) 523
44.8%
ValueCountFrequency (%)
0 502
43.0%
4 1
 
0.1%
10 1
 
0.1%
12 3
 
0.3%
15 1
 
0.1%
16 6
 
0.5%
17 2
 
0.2%
18 4
 
0.3%
20 15
 
1.3%
21 3
 
0.3%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%
364 1
0.1%
341 1
0.1%
319 1
0.1%
312 2
0.2%
304 1
0.1%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2636986
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-13T17:22:34.629616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6814257
Coefficient of variation (CV)0.42808983
Kurtosis-0.37170284
Mean6.2636986
Median Absolute Deviation (MAD)2
Skewness0.23305879
Sum7316
Variance7.1900435
MonotonicityNot monotonic
2025-03-13T17:22:34.724638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 207
17.7%
7 182
15.6%
5 165
14.1%
4 121
10.4%
8 96
8.2%
3 85
7.3%
10 71
 
6.1%
11 60
 
5.1%
9 49
 
4.2%
1 47
 
4.0%
Other values (2) 85
7.3%
ValueCountFrequency (%)
1 47
 
4.0%
2 41
 
3.5%
3 85
7.3%
4 121
10.4%
5 165
14.1%
6 207
17.7%
7 182
15.6%
8 96
8.2%
9 49
 
4.2%
10 71
 
6.1%
ValueCountFrequency (%)
12 44
 
3.8%
11 60
 
5.1%
10 71
 
6.1%
9 49
 
4.2%
8 96
8.2%
7 182
15.6%
6 207
17.7%
5 165
14.1%
4 121
10.4%
3 85
7.3%

YrSold
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2009
273 
2007
271 
2006
245 
2008
228 
2010
151 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4672
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2006
2nd row2010
3rd row2009
4th row2010
5th row2009

Common Values

ValueCountFrequency (%)
2009 273
23.4%
2007 271
23.2%
2006 245
21.0%
2008 228
19.5%
2010 151
12.9%

Length

2025-03-13T17:22:34.830253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:34.927652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2009 273
23.4%
2007 271
23.2%
2006 245
21.0%
2008 228
19.5%
2010 151
12.9%

Most occurring characters

ValueCountFrequency (%)
0 2336
50.0%
2 1168
25.0%
9 273
 
5.8%
7 271
 
5.8%
6 245
 
5.2%
8 228
 
4.9%
1 151
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2336
50.0%
2 1168
25.0%
9 273
 
5.8%
7 271
 
5.8%
6 245
 
5.2%
8 228
 
4.9%
1 151
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2336
50.0%
2 1168
25.0%
9 273
 
5.8%
7 271
 
5.8%
6 245
 
5.2%
8 228
 
4.9%
1 151
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2336
50.0%
2 1168
25.0%
9 273
 
5.8%
7 271
 
5.8%
6 245
 
5.2%
8 228
 
4.9%
1 151
 
3.2%

SaleType
Categorical

Imbalance 

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
WD
1014 
New
 
99
COD
 
34
ConLD
 
6
ConLw
 
4
Other values (4)
 
11

Length

Max length5
Median length2
Mean length2.1541096
Min length2

Characters and Unicode

Total characters2516
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew
2nd rowWD
3rd rowCOD
4th rowWD
5th rowWD

Common Values

ValueCountFrequency (%)
WD 1014
86.8%
New 99
 
8.5%
COD 34
 
2.9%
ConLD 6
 
0.5%
ConLw 4
 
0.3%
CWD 4
 
0.3%
ConLI 3
 
0.3%
Oth 2
 
0.2%
Con 2
 
0.2%

Length

2025-03-13T17:22:35.046650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:35.157743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
wd 1014
86.8%
new 99
 
8.5%
cod 34
 
2.9%
conld 6
 
0.5%
conlw 4
 
0.3%
cwd 4
 
0.3%
conli 3
 
0.3%
oth 2
 
0.2%
con 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
D 1058
42.1%
W 1018
40.5%
w 103
 
4.1%
N 99
 
3.9%
e 99
 
3.9%
C 53
 
2.1%
O 36
 
1.4%
o 15
 
0.6%
n 15
 
0.6%
L 13
 
0.5%
Other values (3) 7
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1058
42.1%
W 1018
40.5%
w 103
 
4.1%
N 99
 
3.9%
e 99
 
3.9%
C 53
 
2.1%
O 36
 
1.4%
o 15
 
0.6%
n 15
 
0.6%
L 13
 
0.5%
Other values (3) 7
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1058
42.1%
W 1018
40.5%
w 103
 
4.1%
N 99
 
3.9%
e 99
 
3.9%
C 53
 
2.1%
O 36
 
1.4%
o 15
 
0.6%
n 15
 
0.6%
L 13
 
0.5%
Other values (3) 7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1058
42.1%
W 1018
40.5%
w 103
 
4.1%
N 99
 
3.9%
e 99
 
3.9%
C 53
 
2.1%
O 36
 
1.4%
o 15
 
0.6%
n 15
 
0.6%
L 13
 
0.5%
Other values (3) 7
 
0.3%

SaleCondition
Categorical

Imbalance 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Normal
954 
Partial
101 
Abnorml
 
85
Family
 
16
Alloca
 
9

Length

Max length7
Median length6
Mean length6.1618151
Min length6

Characters and Unicode

Total characters7197
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPartial
2nd rowNormal
3rd rowAbnorml
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 954
81.7%
Partial 101
 
8.6%
Abnorml 85
 
7.3%
Family 16
 
1.4%
Alloca 9
 
0.8%
AdjLand 3
 
0.3%

Length

2025-03-13T17:22:35.283759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T17:22:35.387697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 954
81.7%
partial 101
 
8.6%
abnorml 85
 
7.3%
family 16
 
1.4%
alloca 9
 
0.8%
adjland 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 1184
16.5%
l 1174
16.3%
r 1140
15.8%
m 1055
14.7%
o 1048
14.6%
N 954
13.3%
i 117
 
1.6%
P 101
 
1.4%
t 101
 
1.4%
A 97
 
1.3%
Other values (8) 226
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1184
16.5%
l 1174
16.3%
r 1140
15.8%
m 1055
14.7%
o 1048
14.6%
N 954
13.3%
i 117
 
1.6%
P 101
 
1.4%
t 101
 
1.4%
A 97
 
1.3%
Other values (8) 226
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1184
16.5%
l 1174
16.3%
r 1140
15.8%
m 1055
14.7%
o 1048
14.6%
N 954
13.3%
i 117
 
1.6%
P 101
 
1.4%
t 101
 
1.4%
A 97
 
1.3%
Other values (8) 226
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1184
16.5%
l 1174
16.3%
r 1140
15.8%
m 1055
14.7%
o 1048
14.6%
N 954
13.3%
i 117
 
1.6%
P 101
 
1.4%
t 101
 
1.4%
A 97
 
1.3%
Other values (8) 226
 
3.1%

Interactions

2025-03-13T17:22:20.310109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.026295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.334014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.630571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.166732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.394991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:09.724997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:11.052142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:12.411343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:13.974444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.211161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.447698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.763617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.064121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:20.400127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.141268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.430030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.717730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.253829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.494210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:09.818008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:11.151358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:12.500297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.065459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.300741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.541507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.857937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.156142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:20.490098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.245899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.520066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.807591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.342757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.587959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:09.915015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:11.253161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:12.591301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.154472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.389011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.639807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.953866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.246267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:20.574142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.331333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.606462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.886175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.421802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.678974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.005027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:11.345170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:12.674319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.237553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.473338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.726754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.041727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.331003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:20.653991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.416418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.694489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.966611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.501634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.764908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.094124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:11.435177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:12.757316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.326561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.554612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.816886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.128023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.416030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:20.760587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.513349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.791243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:06.059618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.598776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.866908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.197048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:11.539196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:13.186521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.423074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.650584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.916866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.227891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.512859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:20.850608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.607939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.887817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:06.466671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.688786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.964926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.292063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:11.640297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:13.279374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.513585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.742209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.015771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.323154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.605052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:20.948176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.706367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.988585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:06.562357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.785380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:09.067927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.391084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:11.745216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:13.374393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.609515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.837458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.116769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.423919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.703630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:21.458226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.792284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.076513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:06.647219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.866804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:09.158943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.478086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:11.835218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:13.454391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.690098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.917645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.204297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.509938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.786144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:21.539238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.878386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.165519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:06.730683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.950815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:09.246553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.570086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:11.928309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:13.534483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.770613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.008655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.294369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.596924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.867151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:21.622814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:03.963485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.251364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:06.812279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.034820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:09.334956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.657094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:12.021245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:13.615474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.852067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.089664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.380892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.684021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:19.952262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:21.718299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.061267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.353540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:06.904721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.127851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:09.438056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.760357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:12.124260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:13.712500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:14.946056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.183760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.482327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.783950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:20.047083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:21.809845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.156993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.449554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:06.997715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.221102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:09.540060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.858119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:12.225840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:13.803425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.039067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.279016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.582893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.881969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:20.138236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:21.893276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:04.246426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:05.541111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:07.082933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:08.308937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:09.631573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:10.951237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:12.318268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:13.889440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:15.125569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:16.363288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:17.672932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:18.972980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-13T17:22:20.224105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-03-13T17:22:35.531712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2ndFlrSFBedroomAbvGrBldgTypeBsmtCondBsmtExposureBsmtFinSF1BsmtFinType1BsmtFinType2BsmtFullBathBsmtQualBsmtUnfSFCentralAirCondition1Condition2ElectricalExterCondExterQualExterior1stExterior2ndFireplacesFoundationFullBathFunctionalGarageCondGarageFinishGarageQualGarageTypeHalfBathHeatingHeatingQCHouseStyleKitchenAbvGrKitchenQualLandContourLandSlopeLotAreaLotConfigLotFrontageLotShapeMSSubClassMSZoningMasVnrAreaMoSoldNeighborhoodOpenPorchSFOverallCondOverallQualPavedDriveRoofMatlRoofStyleSaleConditionSaleTypeStreetUtilitiesWoodDeckSFYearRemodAddYrSold
2ndFlrSF1.0000.4960.1260.0680.153-0.2040.1260.0000.1560.2150.0580.0180.0440.1230.0000.0000.2050.0980.1370.1490.1620.4090.0000.0000.2160.0000.2560.4420.0290.1140.4470.0740.1860.0800.0430.1060.0480.0480.1330.4920.1550.0530.0290.2530.220-0.0150.2760.0890.1260.1170.0500.0000.0000.0000.0740.0700.046
BedroomAbvGr0.4961.0000.2960.1150.090-0.0880.1150.0000.2870.0710.1590.1640.0000.0000.0800.0000.1830.0900.0780.1070.0910.4500.0240.0180.1130.0450.1610.2550.1080.0350.2470.2850.1630.0930.1190.3160.0000.3280.0360.0630.1910.1300.0400.2000.087-0.0230.1220.1110.0960.1520.1340.0000.0000.0000.064-0.0620.000
BldgType0.1260.2961.0000.0210.0470.0000.1050.0160.2010.1770.1010.3090.0790.1580.1000.1070.1850.1540.1960.1180.1870.1090.0660.0000.2010.0600.1300.2550.0770.1140.1500.5960.1580.0880.0430.0400.0700.3150.0830.8460.2050.0000.0350.4050.0000.1340.1270.1380.0310.0440.1550.0700.1380.0000.0740.1970.000
BsmtCond0.0680.1150.0211.0000.0520.0000.0920.0260.0430.2110.0670.2380.0240.0310.4220.2290.1630.1210.0900.0320.1380.1620.2250.1960.1100.2300.0650.0000.0280.0830.0850.0000.1260.0650.1380.0000.0360.0000.0380.0550.0830.0000.0000.1320.0000.4750.3130.1540.0660.0630.0530.1230.0000.0000.0000.1050.063
BsmtExposure0.1530.0900.0470.0521.0000.2360.2000.0920.2400.1990.0990.0770.0750.0000.0550.0000.1610.1110.1280.1230.1340.0880.0210.0680.1790.0530.1310.0600.0000.1000.2230.0410.1480.1960.2340.1510.0600.1150.0900.1930.0600.0870.0400.2670.0070.0950.1790.0820.1380.1320.0930.1190.1080.0000.1860.1600.028
BsmtFinSF1-0.204-0.0880.0000.0000.2361.0000.4090.1080.5010.262-0.5710.1520.0470.1710.0260.0000.2240.1060.1220.1660.1640.1890.0000.0000.2250.0000.1170.0620.0000.0730.1110.0000.2320.1090.1240.1860.0640.1430.119-0.1160.0790.250-0.0280.1790.067-0.0120.1240.1200.0190.0910.1220.1270.0610.0000.1550.0580.000
BsmtFinType10.1260.1150.1050.0920.2000.4091.0000.2270.4340.3180.2640.1860.0510.0110.1050.0690.2790.2000.2050.1130.2850.2260.0960.0740.2420.0680.1470.0000.0300.2010.1560.0800.2770.0910.0450.0000.0580.0340.0330.1810.1230.1020.0150.3000.0560.1710.2240.1630.0670.0660.0950.0990.0000.0000.1100.2560.000
BsmtFinType20.0000.0000.0160.0260.0920.1080.2271.0000.1250.0930.1000.0000.0000.0190.0000.0000.0790.1370.1080.0640.1050.0320.0860.0000.0350.0000.0480.0520.0000.0750.0600.0000.0380.0000.0500.0660.0000.0340.0600.0560.0240.0000.0440.1540.0360.0650.0670.0000.1230.0760.0000.0690.1220.1290.0670.1080.000
BsmtFullBath0.1560.2870.2010.0430.2400.5010.4340.1251.0000.1280.3430.0950.0000.0000.0570.0090.0940.1040.1130.1330.1290.2950.0000.0370.1150.0350.1300.1800.0410.0650.2210.1840.1130.1100.2250.2480.0260.1510.0750.2680.0860.0700.0000.2110.0520.0210.0590.0920.1020.1780.1920.1810.1160.0000.1290.1460.056
BsmtQual0.2150.0710.1770.2110.1990.2620.3180.0930.1281.0000.1860.2220.1660.1430.2050.1170.4640.3190.3140.1730.4100.3390.1470.1470.4100.1600.2470.1600.0000.2870.2170.1170.4260.0850.0000.0000.0940.1240.1340.2730.1820.2200.0000.5300.1560.3240.5110.1830.0000.1550.2630.2630.0000.0000.1850.3890.000
BsmtUnfSF0.0580.1590.1010.0670.099-0.5710.2640.1000.3430.1861.0000.0520.0410.0670.0120.0210.2530.0970.0940.0690.1620.1770.0590.0600.1600.0770.0990.1190.0000.0820.1500.0790.1910.0270.0190.0860.0000.1370.041-0.1320.0610.0870.0520.1760.158-0.1390.2830.0490.0000.0900.1270.0870.0000.000-0.0180.1710.000
CentralAir0.0180.1640.3090.2380.0770.1520.1860.0000.0950.2220.0521.0000.0000.0930.4000.2520.2980.3440.3350.1930.3430.1370.1180.2440.2590.1960.2260.1270.4370.3770.1960.2480.3360.1330.0000.0000.0620.0730.1110.2570.2740.1180.0360.3910.1120.3110.3890.3320.0000.0000.1470.1390.0000.0000.1440.3850.000
Condition10.0440.0000.0790.0240.0750.0470.0510.0000.0000.1660.0410.0001.0000.2170.0000.0480.1350.0820.0860.0260.0870.0630.0000.0000.1480.0000.0980.1010.0000.1950.0910.1080.1010.0000.0000.0000.1500.2040.0940.1070.0790.0000.0140.1810.0640.0560.0580.1240.1010.1100.0000.0000.1800.0000.0890.0990.000
Condition20.1230.0000.1580.0310.0000.1710.0110.0190.0000.1430.0670.0930.2171.0000.0000.3490.1530.0160.0000.0000.0310.1130.0000.0000.0240.1760.1200.2170.0000.0750.1520.1670.1020.0640.0000.0000.1010.0790.0000.1140.0750.1070.0000.0490.2780.1130.1790.0810.0000.3100.0000.0230.0000.0000.0000.0070.000
Electrical0.0000.0800.1000.4220.0550.0260.1050.0000.0570.2050.0120.4000.0000.0001.0000.1390.1410.2420.2030.0780.2080.1250.2190.2570.1820.3550.0860.0820.1420.1490.1270.1750.2210.0580.0000.0000.0000.0000.0260.1270.1090.0000.0000.1800.0100.2770.1720.2030.0000.0000.2170.0000.0000.0960.0340.2340.000
ExterCond0.0000.0000.1070.2290.0000.0000.0690.0000.0090.1170.0210.2520.0480.3490.1391.0000.2100.1200.0840.0320.1340.0910.1660.0750.1230.1410.0000.0560.0620.0760.1450.0090.2110.0000.0000.0000.0000.0000.0000.1150.0850.0000.0000.1620.2000.3860.2310.1810.0000.0650.0560.0770.0000.0000.0380.1140.000
ExterQual0.2050.1830.1850.1630.1610.2240.2790.0790.0940.4640.2530.2980.1350.1530.1410.2101.0000.3470.3560.1910.3740.3160.1140.0420.3940.0320.2450.1490.0490.3340.1870.1250.5490.1400.0900.0000.0000.1270.1130.2440.2360.2600.0550.4840.1640.3230.6450.1940.0000.1510.2350.2840.2500.0000.1670.3890.028
Exterior1st0.0980.0900.1540.1210.1110.1060.2000.1370.1040.3190.0970.3440.0820.0160.2420.1200.3471.0000.7620.1460.3040.2310.1050.1190.3090.0980.1920.1170.1190.2640.1400.2190.2970.0900.1460.0000.0170.0870.0650.1810.1740.0000.0000.2840.0740.1830.1990.1680.1670.1110.1730.1340.0000.0000.0560.2740.018
Exterior2nd0.1370.0780.1960.0900.1280.1220.2050.1080.1130.3140.0940.3350.0860.0000.2030.0840.3560.7621.0000.1120.3090.2200.0900.1220.3190.0990.2030.1970.1980.2660.1450.2370.2970.1140.1360.0740.0570.1080.0780.2050.1940.0000.0000.3250.0750.1690.1990.1470.0970.1440.1620.1270.0000.0000.0580.2660.025
Fireplaces0.1490.1070.1180.0320.1230.1660.1130.0640.1330.1730.0690.1930.0260.0000.0780.0320.1910.1460.1121.0000.1180.1820.0000.0310.2650.0480.1930.1530.0170.0880.0940.1100.1860.0670.1520.1360.0450.1450.1150.1840.1460.1470.0490.3170.1040.0950.2650.1120.0830.0820.0950.0980.0700.0000.1760.1350.024
Foundation0.1620.0910.1870.1380.1340.1640.2850.1050.1290.4100.1620.3430.0870.0310.2080.1340.3740.3040.3090.1181.0000.2870.1140.1370.3750.2030.2110.1670.2430.2930.2180.2080.3540.0880.0560.0000.0360.1150.1170.2710.2250.0710.0000.4130.1250.2470.2920.2010.0000.0940.1680.1470.0370.0000.1270.3200.011
FullBath0.4090.4500.1090.1620.0880.1890.2260.0320.2950.3390.1770.1370.0630.1130.1250.0910.3160.2310.2200.1820.2871.0000.0760.0340.3190.0530.2610.1900.0060.2140.2420.1500.2890.1040.1470.1020.0360.1150.1000.2450.1880.1510.0510.3670.1590.3270.4140.0900.0910.1540.2020.1340.0000.0000.1470.2680.000
Functional0.0000.0240.0660.2250.0210.0000.0960.0860.0000.1470.0590.1180.0000.0000.2190.1660.1140.1050.0900.0000.1140.0761.0000.0720.1060.1180.1750.0480.1000.0150.0360.0000.0950.0000.0500.0000.0000.0000.0000.0840.0000.0000.0270.1060.0910.1750.1320.0940.1860.1550.0310.0360.0000.0000.0290.0540.047
GarageCond0.0000.0180.0000.1960.0680.0000.0740.0000.0370.1470.0600.2440.0000.0000.2570.0750.0420.1190.1220.0310.1370.0340.0721.0000.1200.6840.1220.0240.1820.0480.1500.2880.0950.0000.0000.0000.0280.0000.0230.0310.0580.0000.0390.1020.0000.1110.0610.1930.0610.0490.0000.0000.0000.0000.0180.1120.007
GarageFinish0.2160.1130.2010.1100.1790.2250.2420.0350.1150.4100.1600.2590.1480.0240.1820.1230.3940.3090.3190.2650.3750.3190.1060.1201.0000.1380.4050.1930.0710.2960.2420.1350.3820.1240.0160.0290.0500.1640.1840.3430.2230.2040.0210.4850.1740.2540.4310.1680.0000.1020.2070.2000.0000.0170.2100.3510.000
GarageQual0.0000.0450.0600.2300.0530.0000.0680.0000.0350.1600.0770.1960.0000.1760.3550.1410.0320.0980.0990.0480.2030.0530.1180.6840.1381.0000.1250.0000.1260.0630.1070.1180.0890.0000.0000.0000.0000.0000.0250.0760.0910.0000.0000.1450.0620.1440.0380.1410.0000.0000.0000.0000.0000.0000.0000.1140.045
GarageType0.2560.1610.1300.0650.1310.1170.1470.0480.1300.2470.0990.2260.0980.1200.0860.0000.2450.1920.2030.1930.2110.2610.1750.1220.4050.1251.0000.2150.0830.1450.2120.1620.2020.0930.1140.0660.0370.1160.1330.2530.1810.0890.0000.2830.0860.1590.1920.1030.0360.0700.1180.0980.2490.2480.1120.1840.000
HalfBath0.4420.2550.2550.0000.0600.0620.0000.0520.1800.1600.1190.1270.1010.2170.0820.0560.1490.1170.1970.1530.1670.1900.0480.0240.1930.0000.2151.0000.0000.1040.4610.2210.1490.0350.0460.0000.0070.0000.0840.5230.1440.1340.0710.2830.1330.0970.2220.0910.0000.2390.1590.0280.0000.0000.0630.2130.000
Heating0.0290.1080.0770.0280.0000.0000.0300.0000.0410.0000.0000.4370.0000.0000.1420.0620.0490.1190.1980.0170.2430.0060.1000.1820.0710.1260.0830.0001.0000.2120.1220.0870.1650.0000.0350.1410.0000.0800.0540.0760.0600.0000.0000.0940.1300.1230.2000.1220.0000.0310.0000.0980.0000.0000.0000.0860.032
HeatingQC0.1140.0350.1140.0830.1000.0730.2010.0750.0650.2870.0820.3770.1950.0750.1490.0760.3340.2640.2660.0880.2930.2140.0150.0480.2960.0630.1450.1040.2121.0000.1830.1330.3300.0690.0740.0000.0000.0440.0490.1710.1250.0390.0400.3050.1030.1870.2600.1680.0000.0000.1420.1290.0000.0340.1070.3340.000
HouseStyle0.4470.2470.1500.0850.2230.1110.1560.0600.2210.2170.1500.1960.0910.1520.1270.1450.1870.1400.1450.0940.2180.2420.0360.1500.2420.1070.2120.4610.1220.1831.0000.2660.1430.1500.0400.0000.0000.0300.0500.6130.1810.0390.0110.2800.1510.1110.1380.1710.0000.1060.0920.0110.0270.1150.0610.1970.000
KitchenAbvGr0.0740.2850.5960.0000.0410.0000.0800.0000.1840.1170.0790.2480.1080.1670.1750.0090.1250.2190.2370.1100.2080.1500.0000.2880.1350.1180.1620.2210.0870.1330.2661.0000.1320.0130.0000.0000.0000.0300.0370.5860.1130.0000.0200.1320.0280.0870.1250.1360.0600.0560.2180.0270.0000.0000.0000.1620.000
KitchenQual0.1860.1630.1580.1260.1480.2320.2770.0380.1130.4260.1910.3360.1010.1020.2210.2110.5490.2970.2970.1860.3540.2890.0950.0950.3820.0890.2020.1490.1650.3300.1430.1321.0000.1080.0480.0000.0000.1090.0860.2250.1710.2020.0450.4420.1420.2440.5570.2080.0000.1170.2300.2280.0430.0000.1820.4210.000
LandContour0.0800.0930.0880.0650.1960.1090.0910.0000.1100.0850.0270.1330.0000.0640.0580.0000.1400.0900.1140.0670.0880.1040.0000.0000.1240.0000.0930.0350.0000.0690.1500.0130.1081.0000.4720.2670.0650.1450.1410.1020.0970.0330.0950.3820.0000.1110.1780.1090.1690.1590.1100.0000.0990.0000.0660.1270.000
LandSlope0.0430.1190.0430.1380.2340.1240.0450.0500.2250.0000.0190.0000.0000.0000.0000.0000.0900.1460.1360.1520.0560.1470.0500.0000.0160.0000.1140.0460.0350.0740.0400.0000.0480.4721.0000.4750.0900.1500.1320.0000.0210.0080.0000.3180.0000.1930.1530.0000.3460.3210.0340.0000.1650.0000.1380.1040.000
LotArea0.1060.3160.0400.0000.1510.1860.0000.0660.2480.0000.0860.0000.0000.0000.0000.0000.0000.0000.0740.1360.0000.1020.0000.0000.0290.0000.0660.0000.1410.0000.0000.0000.0000.2670.4751.0000.0730.6420.321-0.2750.0000.1890.0020.1770.155-0.0510.2510.0000.2420.1440.0000.0000.3200.0000.1750.0880.000
LotConfig0.0480.0000.0700.0360.0600.0640.0580.0000.0260.0940.0000.0620.1500.1010.0000.0000.0000.0170.0570.0450.0360.0360.0000.0280.0500.0000.0370.0070.0000.0000.0000.0000.0000.0650.0900.0731.0000.1760.2200.0610.0580.0000.0460.1330.0000.0000.0000.0370.0660.0450.0200.0270.0000.0930.0480.0820.026
LotFrontage0.0480.3280.3150.0000.1150.1430.0340.0340.1510.1240.1370.0730.2040.0790.0000.0000.1270.0870.1080.1450.1150.1150.0000.0000.1640.0000.1160.0000.0800.0440.0300.0300.1090.1450.1500.6420.1761.0000.333-0.3070.1890.2550.0170.2430.156-0.0720.2600.0800.2180.2370.0620.0000.1451.0000.1040.1100.000
LotShape0.1330.0360.0830.0380.0900.1190.0330.0600.0750.1340.0410.1110.0940.0000.0260.0000.1130.0650.0780.1150.1170.1000.0000.0230.1840.0250.1330.0840.0540.0490.0500.0370.0860.1410.1320.3210.2200.3331.0000.1220.1480.0640.0000.2360.0000.0550.1100.0780.0480.0440.0310.0000.0410.0000.1200.1310.014
MSSubClass0.4920.0630.8460.0550.193-0.1160.1810.0560.2680.273-0.1320.2570.1070.1140.1270.1150.2440.1810.2050.1840.2710.2450.0840.0310.3430.0760.2530.5230.0760.1710.6130.5860.2250.1020.000-0.2750.061-0.3070.1221.0000.2740.0010.0120.4070.028-0.0820.0760.1570.0350.1210.1530.0740.1240.0000.004-0.0180.000
MSZoning0.1550.1910.2050.0830.0600.0790.1230.0240.0860.1820.0610.2740.0790.0750.1090.0850.2360.1740.1940.1460.2250.1880.0000.0580.2230.0910.1810.1440.0600.1250.1810.1130.1710.0970.0210.0000.0580.1890.1480.2741.0000.0740.0290.6330.1540.1760.2130.2120.0000.0870.1420.1770.1550.0000.0500.2010.000
MasVnrArea0.0530.1300.0000.0000.0870.2500.1020.0000.0700.2200.0870.1180.0000.1070.0000.0000.2600.0000.0000.1470.0710.1510.0000.0000.2040.0000.0890.1340.0000.0390.0390.0000.2020.0330.0080.1890.0000.2550.0640.0010.0741.0000.0040.1900.192-0.1670.4230.0650.1550.1070.0800.0550.0000.1890.1540.2230.042
MoSold0.0290.0400.0350.0000.040-0.0280.0150.0440.0000.0000.0520.0360.0140.0000.0000.0000.0550.0000.0000.0490.0000.0510.0270.0390.0210.0000.0000.0710.0000.0400.0110.0200.0450.0950.0000.0020.0460.0170.0000.0120.0290.0041.0000.0590.064-0.0050.0480.0000.0000.0170.0470.0170.0860.0530.0490.0350.157
Neighborhood0.2530.2000.4050.1320.2670.1790.3000.1540.2110.5300.1760.3910.1810.0490.1800.1620.4840.2840.3250.3170.4130.3670.1060.1020.4850.1450.2830.2830.0940.3050.2800.1320.4420.3820.3180.1770.1330.2430.2360.4070.6330.1900.0591.0000.1080.2220.3210.3120.1030.1860.2320.1820.1780.1040.1640.3830.000
OpenPorchSF0.2200.0870.0000.0000.0070.0670.0560.0360.0520.1560.1580.1120.0640.2780.0100.2000.1640.0740.0750.1040.1250.1590.0910.0000.1740.0620.0860.1330.1300.1030.1510.0280.1420.0000.0000.1550.0000.1560.0000.0280.1540.1920.0640.1081.000-0.1380.4250.0360.0000.0000.0600.0560.0540.0470.0790.3430.000
OverallCond-0.015-0.0230.1340.4750.095-0.0120.1710.0650.0210.324-0.1390.3110.0560.1130.2770.3860.3230.1830.1690.0950.2470.3270.1750.1110.2540.1440.1590.0970.1230.1870.1110.0870.2440.1110.193-0.0510.000-0.0720.055-0.0820.176-0.167-0.0050.222-0.1381.000-0.1870.1970.0000.0320.1190.1180.1010.000-0.012-0.0200.037
OverallQual0.2760.1220.1270.3130.1790.1240.2240.0670.0590.5110.2830.3890.0580.1790.1720.2310.6450.1990.1990.2650.2920.4140.1320.0610.4310.0380.1920.2220.2000.2600.1380.1250.5570.1780.1530.2510.0000.2600.1100.0760.2130.4230.0480.3210.425-0.1871.0000.1960.0000.1130.1600.1890.1000.0000.2450.5720.017
PavedDrive0.0890.1110.1380.1540.0820.1200.1630.0000.0920.1830.0490.3320.1240.0810.2030.1810.1940.1680.1470.1120.2010.0900.0940.1930.1680.1410.1030.0910.1220.1680.1710.1360.2080.1090.0000.0000.0370.0800.0780.1570.2120.0650.0000.3120.0360.1970.1961.0000.0000.1160.1400.1040.0000.0000.0560.1630.000
RoofMatl0.1260.0960.0310.0660.1380.0190.0670.1230.1020.0000.0000.0000.1010.0000.0000.0000.0000.1670.0970.0830.0000.0910.1860.0610.0000.0000.0360.0000.0000.0000.0000.0600.0000.1690.3460.2420.0660.2180.0480.0350.0000.1550.0000.1030.0000.0000.0000.0001.0000.4930.0000.0000.0000.0000.1180.0560.000
RoofStyle0.1170.1520.0440.0630.1320.0910.0660.0760.1780.1550.0900.0000.1100.3100.0000.0650.1510.1110.1440.0820.0940.1540.1550.0490.1020.0000.0700.2390.0310.0000.1060.0560.1170.1590.3210.1440.0450.2370.0440.1210.0870.1070.0170.1860.0000.0320.1130.1160.4931.0000.0990.0000.0000.0000.0500.0960.000
SaleCondition0.0500.1340.1550.0530.0930.1220.0950.0000.1920.2630.1270.1470.0000.0000.2170.0560.2350.1730.1620.0950.1680.2020.0310.0000.2070.0000.1180.1590.0000.1420.0920.2180.2300.1100.0340.0000.0200.0620.0310.1530.1420.0800.0470.2320.0600.1190.1600.1400.0000.0991.0000.4680.0000.0810.0000.2620.082
SaleType0.0000.0000.0700.1230.1190.1270.0990.0690.1810.2630.0870.1390.0000.0230.0000.0770.2840.1340.1270.0980.1470.1340.0360.0000.2000.0000.0980.0280.0980.1290.0110.0270.2280.0000.0000.0000.0270.0000.0000.0740.1770.0550.0170.1820.0560.1180.1890.1040.0000.0000.4681.0000.1600.1470.0160.2090.087
Street0.0000.0000.1380.0000.1080.0610.0000.1220.1160.0000.0000.0000.1800.0000.0000.0000.2500.0000.0000.0700.0370.0000.0000.0000.0000.0000.2490.0000.0000.0000.0270.0000.0430.0990.1650.3200.0000.1450.0410.1240.1550.0000.0860.1780.0540.1010.1000.0000.0000.0000.0000.1601.0000.0000.2440.1360.010
Utilities0.0000.0000.0000.0000.0000.0000.0000.1290.0000.0000.0000.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.2480.0000.0000.0340.1150.0000.0000.0000.0000.0000.0931.0000.0000.0000.0000.1890.0530.1040.0470.0000.0000.0000.0000.0000.0810.1470.0001.0000.0000.0960.000
WoodDeckSF0.0740.0640.0740.0000.1860.1550.1100.0670.1290.185-0.0180.1440.0890.0000.0340.0380.1670.0560.0580.1760.1270.1470.0290.0180.2100.0000.1120.0630.0000.1070.0610.0000.1820.0660.1380.1750.0480.1040.1200.0040.0500.1540.0490.1640.079-0.0120.2450.0560.1180.0500.0000.0160.2440.0001.0000.2330.022
YearRemodAdd0.070-0.0620.1970.1050.1600.0580.2560.1080.1460.3890.1710.3850.0990.0070.2340.1140.3890.2740.2660.1350.3200.2680.0540.1120.3510.1140.1840.2130.0860.3340.1970.1620.4210.1270.1040.0880.0820.1100.131-0.0180.2010.2230.0350.3830.343-0.0200.5720.1630.0560.0960.2620.2090.1360.0960.2331.0000.000
YrSold0.0460.0000.0000.0630.0280.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0280.0180.0250.0240.0110.0000.0470.0070.0000.0450.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0260.0000.0140.0000.0000.0420.1570.0000.0000.0370.0170.0000.0000.0000.0820.0870.0100.0000.0220.0001.000

Missing values

2025-03-13T17:22:22.100293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-13T17:22:22.419327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-13T17:22:22.703260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MSSubClassMSZoningLotFrontageLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtUnfSFHeatingHeatingQCCentralAirElectrical2ndFlrSFBsmtFullBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualFunctionalFireplacesGarageTypeGarageFinishGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFMoSoldYrSoldSaleTypeSaleCondition
66420RL49.020896PaveIR2LvlAllPubCulDSacGtlSomerstRRAnNorm1Fam1Story852006GableCompShgVinylSdVinylSd0.0GdTAPConcExTAMnGLQ1721Unf356GasAExYSBrkr011111ExTyp1AttchdFinTATAY19226712006NewPartial
62520RL87.010000PaveIR1LvlAllPubCornerGtlNAmesNormNorm1Fam1Story661962HipCompShgWd SdngWd Sdng261.0TATACBlockTATANoUnf0Unf1116GasATAYSBrkr001131TATyp0AttchdUnfTATAY0022010WDNormal
112220RLNaN8926PaveIR1LvlAllPubCornerGtlEdwardsNormNorm1Fam1Story431956GableCompShgAsbShngAsbShng0.0TATACBlockTATANoUnf0Unf672GasAExYFuseA001031TATyp0BasmentUnfTATAY640102009CODAbnorml
861190RL75.011625PaveRegLvlAllPubInsideGtlSawyerNormNorm2fmCon1Story541965HipCompShgPlywoodHdBoard0.0TATAPConcTATAMnBLQ841Unf198GasAExYSBrkr011131TATyp0AttchdUnfTATAY0042010WDNormal
47820RL79.010637PaveRegLvlAllPubInsideGtlCollgCrNormNorm1Fam1Story852008HipCompShgVinylSdVinylSd336.0GdTAPConcExTAGdGLQ1288Unf417GasAExYSBrkr012031GdTyp1AttchdRFnTATAY2084492009WDNormal
219120RL43.03010PaveRegLvlAllPubInsideGtlBlmngtnNormNormTwnhsE1Story752006GableCompShgVinylSdVinylSd16.0GdTAPConcGdTAAvGLQ16Unf1232GasAExYSBrkr002021GdTyp0AttchdFinTATAY108032006NewPartial
189120RL41.04923PaveRegLvlAllPubInsideGtlStoneBrNormNormTwnhsE1Story852002GableCompShgCemntBdCmentBd0.0GdTAPConcExTAAvGLQ1153Unf440GasAExYSBrkr011101ExTyp1AttchdFinTATAY012082008WDNormal
73260RL75.011404PaveIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story751999GableCompShgVinylSdVinylSd202.0GdTAPConcGdTAAvALQ252Unf901GasAExYSBrkr87802131GdTyp1AttchdFinTATAY1928472008WDNormal
72220RL70.08120PaveRegLvlAllPubInsideGtlNAmesNormNorm1Fam1Story471970GableCompShgMetalSdMetalSd0.0TAGdCBlockTATANoALQ191Unf673GasAExYSBrkr001031TATyp0DetchdUnfTATAY0072009WDNormal
107020RL72.010152PaveRegLvlAllPubInsideGtlNAmesNormNorm1Fam1Story551956HipCompShgMetalSdMetalSd120.0TATACBlockTATANoBLQ586Unf462GasATAYSBrkr011031TATyp0AttchdUnfTATAY02062007WDNormal
MSSubClassMSZoningLotFrontageLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtUnfSFHeatingHeatingQCCentralAirElectrical2ndFlrSFBsmtFullBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualFunctionalFireplacesGarageTypeGarageFinishGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFMoSoldYrSoldSaleTypeSaleCondition
133350RM60.07200PaveRegLvlAllPubCornerGtlIDOTRRNormNorm1Fam1.5Fin561995GableCompShgWd SdngWd Sdng0.0TATACBlockTATANoUnf0Unf803GasAExYSBrkr55701121GdTyp0DetchdUnfTATAY06572006WDNormal
69520RL54.013811PaveIR1LvlAllPubInsideGtlTimberNormNorm1Fam1Story661987GableCompShgHdBoardHdBoard72.0TATACBlockGdGdNoGLQ980LwQ92GasAGdYSBrkr012021GdTyp1AttchdUnfTATAY125072006WDNormal
142520RL80.010721PaveIR1LvlAllPubInsideGtlNAmesNormNorm1Fam1Story661959HipCompShgHdBoardHdBoard243.0GdTACBlockTATANoUnf0Unf1252GasAExYSBrkr001031GdTyp0DetchdUnfTATAY039102008WDNormal
94160RLNaN8755PaveIR1LvlAllPubFR2GtlGilbertRRNnNorm1Fam2Story751999GableCompShgVinylSdVinylSd298.0GdTAPConcGdTANoALQ772Unf220GasAExYSBrkr103812131GdTyp1BuiltInRFnTATAY0062009WDNormal
16020RLNaN11120PaveIR1LvlAllPubCulDSacGtlVeenkerNormNorm1Fam1Story661984GableCompShgPlywoodPlywood0.0TATAPConcGdTANoBLQ660Unf572GasATAYSBrkr002031TATyp0AttchdUnfTATAY0062008WDNormal
110120RL61.09758PaveIR1LvlAllPubInsideGtlNAmesNormNorm1Fam1Story551971GableCompShgHdBoardMetalSd0.0TATACBlockTATANoBLQ412LwQ251GasATAYSBrkr001031TATyp0DetchdUnfTATAY0072007WDNormal
1126120RL53.03684PaveRegLvlAllPubInsideGtlBlmngtnNormNormTwnhsE1Story752007HipCompShgVinylSdVinylSd130.0GdTAPConcGdTANoUnf0Unf1373GasAExYSBrkr002021GdTyp1AttchdFinTATAY1432062009WDNormal
145620RL85.013175PaveRegLvlAllPubInsideGtlNWAmesNormNorm1Fam1Story661988GableCompShgPlywoodPlywood119.0TATACBlockGdTANoALQ790Rec589GasATAYSBrkr012031TAMin12AttchdUnfTATAY349022010WDNormal
134260RLNaN9375PaveRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story852002GableCompShgVinylSdVinylSd149.0GdTAPConcGdTANoUnf0Unf1284GasAExYSBrkr88502131GdTyp1AttchdRFnTATAY1928782007WDNormal
82120RM60.06000PaveRegBnkAllPubInsideModOldTownNormNorm2fmCon1Story441953GableCompShgMetalSdMetalSd0.0FaTACBlockFaTANoUnf0Unf936GasATANSBrkr001021TAMin20DetchdUnfTATAY03222009WDNormal